glm-4 checkpoints

llama-factory/merged_models/internlm2_5-7b-chat-1m_sft_bf16_p2_full/added_tokens.json

@@ -1,8 +0,0 @@
-{
-  "[UNUSED_TOKEN_141]": 92544,
-  "[UNUSED_TOKEN_142]": 92545,
-  "[UNUSED_TOKEN_143]": 92546,
-  "[UNUSED_TOKEN_144]": 92547,
-  "[UNUSED_TOKEN_145]": 92548,
-  "[UNUSED_TOKEN_146]": 92549
-}

llama-factory/merged_models/internlm2_5-7b-chat-1m_sft_bf16_p2_full/config.json

@@ -1,37 +0,0 @@
-{
-  "_name_or_path": "internlm/internlm2_5-7b-chat-1m",
-  "architectures": [
-    "InternLM2ForCausalLM"
-  ],
-  "attn_implementation": "eager",
-  "auto_map": {
-    "AutoConfig": "configuration_internlm2.InternLM2Config",
-    "AutoModel": "modeling_internlm2.InternLM2ForCausalLM",
-    "AutoModelForCausalLM": "internlm/internlm2_5-7b-chat-1m--modeling_internlm2.InternLM2ForCausalLM"
-  },
-  "bias": false,
-  "bos_token_id": 1,
-  "eos_token_id": 2,
-  "hidden_act": "silu",
-  "hidden_size": 4096,
-  "initializer_range": 0.02,
-  "intermediate_size": 14336,
-  "max_position_embeddings": 262144,
-  "model_type": "internlm2",
-  "num_attention_heads": 32,
-  "num_hidden_layers": 32,
-  "num_key_value_heads": 8,
-  "pad_token_id": 2,
-  "pretraining_tp": 1,
-  "rms_norm_eps": 1e-05,
-  "rope_scaling": {
-    "factor": 2.5,
-    "type": "dynamic"
-  },
-  "rope_theta": 50000000,
-  "tie_word_embeddings": false,
-  "torch_dtype": "bfloat16",
-  "transformers_version": "4.42.3",
-  "use_cache": true,
-  "vocab_size": 92544
-}

llama-factory/merged_models/internlm2_5-7b-chat-1m_sft_bf16_p2_full/configuration_internlm2.py

@@ -1,180 +0,0 @@
-# coding=utf-8
-# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
-#
-# This code is based on transformers/src/transformers/models/llama/configuration_llama.py
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" InternLM2 model configuration"""
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.utils import logging
-
-logger = logging.get_logger(__name__)
-
-INTERNLM2_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
-
-
-# Modified from transformers.model.llama.configuration_llama.LlamaConfig
-class InternLM2Config(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`InternLM2Model`]. It is used to instantiate
-    an InternLM2 model according to the specified arguments, defining the model architecture. Instantiating a
-    configuration with the defaults will yield a similar configuration to that of the InternLM2-7B.
-
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
-
-
-    Args:
-        vocab_size (`int`, *optional*, defaults to 32000):
-            Vocabulary size of the InternLM2 model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`InternLM2Model`]
-        hidden_size (`int`, *optional*, defaults to 4096):
-            Dimension of the hidden representations.
-        intermediate_size (`int`, *optional*, defaults to 11008):
-            Dimension of the MLP representations.
-        num_hidden_layers (`int`, *optional*, defaults to 32):
-            Number of hidden layers in the Transformer decoder.
-        num_attention_heads (`int`, *optional*, defaults to 32):
-            Number of attention heads for each attention layer in the Transformer decoder.
-        num_key_value_heads (`int`, *optional*):
-            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
-            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
-            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
-            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
-            by meanpooling all the original heads within that group. For more details checkout [this
-            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
-            `num_attention_heads`.
-        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
-            The non-linear activation function (function or string) in the decoder.
-        max_position_embeddings (`int`, *optional*, defaults to 2048):
-            The maximum sequence length that this model might ever be used with. InternLM2 supports up to 32768 tokens.
-        initializer_range (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
-            The epsilon used by the rms normalization layers.
-        use_cache (`bool`, *optional*, defaults to `True`):
-            Whether or not the model should return the last key/values attentions (not used by all models). Only
-            relevant if `config.is_decoder=True`.
-        pad_token_id (`int`, *optional*):
-            Padding token id.
-        bos_token_id (`int`, *optional*, defaults to 1):
-            Beginning of stream token id.
-        eos_token_id (`int`, *optional*, defaults to 2):
-            End of stream token id.
-        pretraining_tp (`int`, *optional*, defaults to 1):
-            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
-            document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism)
-            to understand more about it. This value is necessary to ensure exact reproducibility
-            of the pretraining results. Please refer to [this
-            issue](https://github.com/pytorch/pytorch/issues/76232).
-        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
-            Whether to tie weight embeddings
-        rope_theta (`float`, *optional*, defaults to 10000.0):
-            The base period of the RoPE embeddings.
-        rope_scaling (`Dict`, *optional*):
-            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
-            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
-            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
-            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
-            these scaling strategies behave:
-            https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
-            experimental feature, subject to breaking API changes in future versions.
-    """
-    _auto_class = "AutoConfig"
-    model_type = "internlm2"
-    keys_to_ignore_at_inference = ["past_key_values"]
-
-    def __init__(  # pylint: disable=W0102
-        self,
-        vocab_size=103168,
-        hidden_size=4096,
-        intermediate_size=11008,
-        num_hidden_layers=32,
-        num_attention_heads=32,
-        num_key_value_heads=None,
-        hidden_act="silu",
-        max_position_embeddings=2048,
-        initializer_range=0.02,
-        rms_norm_eps=1e-6,
-        use_cache=True,
-        pad_token_id=0,
-        bos_token_id=1,
-        eos_token_id=2,
-        pretraining_tp=1,
-        tie_word_embeddings=False,
-        bias=True,
-        rope_theta=10000,
-        rope_scaling=None,
-        attn_implementation=None,
-        **kwargs,
-    ):
-        self.vocab_size = vocab_size
-        self.max_position_embeddings = max_position_embeddings
-        self.hidden_size = hidden_size
-        self.intermediate_size = intermediate_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-        self.bias = bias
-
-        if num_key_value_heads is None:
-            num_key_value_heads = num_attention_heads
-        self.num_key_value_heads = num_key_value_heads
-
-        self.hidden_act = hidden_act
-        self.initializer_range = initializer_range
-        self.rms_norm_eps = rms_norm_eps
-        self.pretraining_tp = pretraining_tp
-        self.use_cache = use_cache
-        self.rope_theta = rope_theta
-        self.rope_scaling = rope_scaling
-        self._rope_scaling_validation()
-        self.attn_implementation = attn_implementation
-        if self.attn_implementation is None:
-            self.attn_implementation = "eager"
-
-        super().__init__(
-            pad_token_id=pad_token_id,
-            bos_token_id=bos_token_id,
-            eos_token_id=eos_token_id,
-            tie_word_embeddings=tie_word_embeddings,
-            **kwargs,
-        )
-
-    def _rope_scaling_validation(self):
-        """
-        Validate the `rope_scaling` configuration.
-        """
-        if self.rope_scaling is None:
-            return
-
-        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
-            raise ValueError(
-                "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
-                f"got {self.rope_scaling}"
-            )
-        rope_scaling_type = self.rope_scaling.get("type", None)
-        rope_scaling_factor = self.rope_scaling.get("factor", None)
-        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
-            raise ValueError(
-                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
-            )
-        if (
-            rope_scaling_factor is None
-            or not isinstance(rope_scaling_factor, (float, int))
-            or rope_scaling_factor < 1.0
-        ):
-            raise ValueError(
-                f"`rope_scaling`'s factor field must be a number >= 1, got {rope_scaling_factor} "
-                f"of type {type(rope_scaling_factor)}"
-            )

llama-factory/merged_models/internlm2_5-7b-chat-1m_sft_bf16_p2_full/generation_config.json

@@ -1,9 +0,0 @@
-{
-  "bos_token_id": 1,
-  "eos_token_id": [
-    2,
-    92542
-  ],
-  "pad_token_id": 2,
-  "transformers_version": "4.42.3"
-}

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llama-factory/merged_models/internlm2_5-7b-chat-1m_sft_bf16_p2_full/modeling_internlm2.py

@@ -1,1800 +0,0 @@
-# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
-#
-# This code is based on transformers/src/transformers/models/llama/modeling_llama.py
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""PyTorch InternLM2.5 model."""
-import math
-import queue
-import threading
-from typing import List, Optional, Tuple, Union
-
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint
-from einops import rearrange
-from torch import nn
-from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
-from transformers.activations import ACT2FN
-from transformers.cache_utils import Cache, DynamicCache, StaticCache
-from transformers.modeling_attn_mask_utils import AttentionMaskConverter
-from transformers.modeling_outputs import (
-    BaseModelOutputWithPast,
-    CausalLMOutputWithPast,
-    QuestionAnsweringModelOutput,
-    SequenceClassifierOutputWithPast,
-    TokenClassifierOutput,
-)
-from transformers.modeling_utils import PreTrainedModel
-from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
-from transformers.utils import (
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    is_flash_attn_greater_or_equal_2_10,
-    logging,
-    replace_return_docstrings,
-)
-
-try:
-    from transformers.generation.streamers import BaseStreamer
-except Exception:
-    BaseStreamer = None
-
-from .configuration_internlm2 import InternLM2Config
-
-
-try:
-    from flash_attn import flash_attn_func, flash_attn_varlen_func
-    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input
-except:
-    pass
-
-
-logger = logging.get_logger(__name__)
-
-_CONFIG_FOR_DOC = "InternLM2Config"
-
-
-def _get_unpad_data(attention_mask):
-    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
-    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
-    max_seqlen_in_batch = seqlens_in_batch.max().item()
-    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))  # pylint: disable=E1102
-    return (
-        indices,
-        cu_seqlens,
-        max_seqlen_in_batch,
-    )
-
-
-class InternLM2RMSNorm(nn.Module):
-    """InternLM2RMSNorm is equivalent to T5LayerNorm."""
-
-    def __init__(self, hidden_size, eps=1e-6):
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(hidden_size))
-        self.variance_epsilon = eps
-
-    def forward(self, hidden_states):
-        input_dtype = hidden_states.dtype
-        hidden_states = hidden_states.to(torch.float32)
-        variance = hidden_states.pow(2).mean(-1, keepdim=True)
-        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
-        return self.weight * hidden_states.to(input_dtype)
-
-
-ALL_LAYERNORM_LAYERS.append(InternLM2RMSNorm)
-
-
-class InternLM2RotaryEmbedding(nn.Module):
-    """Rotary Position Embedding for the InternLM2 model. Credits to the Reddit user /u/lucidrains."""
-
-    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
-        super().__init__()
-        self.scaling_factor = scaling_factor
-        self.dim = dim
-        self.max_position_embeddings = max_position_embeddings
-        self.base = base
-        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        # For BC we register cos and sin cached
-        self.max_seq_len_cached = max_position_embeddings
-
-    @torch.no_grad()
-    def forward(self, x, position_ids):
-        # x: [bs, num_attention_heads, seq_len, head_size]
-        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
-        position_ids_expanded = position_ids[:, None, :].float()
-        # Force float32 since bfloat16 loses precision on long contexts
-        # See https://github.com/huggingface/transformers/pull/29285
-        device_type = x.device.type
-        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
-        with torch.autocast(device_type=device_type, enabled=False):
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
-            emb = torch.cat((freqs, freqs), dim=-1)
-            cos = emb.cos()
-            sin = emb.sin()
-        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
-
-
-class InternLM2LinearScalingRotaryEmbedding(InternLM2RotaryEmbedding):
-    """InternLM2RotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
-
-    def forward(self, x, position_ids):
-        # difference to the original RoPE: a scaling factor is aplied to the position ids
-        position_ids = position_ids.float() / self.scaling_factor
-        cos, sin = super().forward(x, position_ids)
-        return cos, sin
-
-
-class InternLM2DynamicNTKScalingRotaryEmbedding(InternLM2RotaryEmbedding):
-    """InternLM2RotaryEmbedding extended with Dynamic NTK scaling.
-    Credits to the Reddit users /u/bloc97 and /u/emozilla"""
-
-    def forward(self, x, position_ids):
-        # difference to the original RoPE: inv_freq is recomputed when the sequence length > original length
-        seq_len = torch.max(position_ids) + 1
-        if seq_len > self.max_position_embeddings:
-            base = self.base * (
-                (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
-            ) ** (self.dim / (self.dim - 2))
-            inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(x.device) / self.dim))
-            self.register_buffer("inv_freq", inv_freq, persistent=False)  # TODO joao: this may break with compilation
-
-        cos, sin = super().forward(x, position_ids)
-        return cos, sin
-
-
-def rotate_half(x):
-    """Rotates half the hidden dims of the input."""
-    x1 = x[..., : x.shape[-1] // 2]
-    x2 = x[..., x.shape[-1] // 2 :]
-    return torch.cat((-x2, x1), dim=-1)
-
-
-def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):  # pylint: disable=unused-argument
-    """Applies Rotary Position Embedding to the query and key tensors.
-
-    Args:
-        q (`torch.Tensor`): The query tensor.
-        k (`torch.Tensor`): The key tensor.
-        cos (`torch.Tensor`): The cosine part of the rotary embedding.
-        sin (`torch.Tensor`): The sine part of the rotary embedding.
-        position_ids (`torch.Tensor`, *optional*):
-            Deprecated and unused.
-        unsqueeze_dim (`int`, *optional*, defaults to 1):
-            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
-            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
-            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
-            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
-            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
-            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
-    Returns:
-        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
-    """
-    cos = cos.unsqueeze(unsqueeze_dim)
-    sin = sin.unsqueeze(unsqueeze_dim)
-    q_embed = (q * cos) + (rotate_half(q) * sin)
-    k_embed = (k * cos) + (rotate_half(k) * sin)
-    return q_embed, k_embed
-
-
-class InternLM2MLP(nn.Module):
-    """MLP for InternLM2 model."""
-
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.hidden_size = config.hidden_size
-        self.intermediate_size = config.intermediate_size
-        self.w1 = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
-        self.w3 = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
-        self.w2 = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
-        self.act_fn = ACT2FN[config.hidden_act]
-
-    def forward(self, x):
-        down_proj = self.w2(self.act_fn(self.w1(x)) * self.w3(x))
-
-        return down_proj
-
-
-def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
-    """
-    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
-    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
-    """
-    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
-    if n_rep == 1:
-        return hidden_states
-    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
-    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
-
-
-class InternLM2Attention(nn.Module):
-    """Multi-headed attention from 'Attention Is All You Need' paper"""
-
-    def __init__(self, config: InternLM2Config, layer_idx: Optional[int] = None):
-        super().__init__()
-        self.config = config
-        self.layer_idx = layer_idx
-        if layer_idx is None:
-            logger.warning_once(
-                f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
-                "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
-                "when creating this class."
-            )
-
-        self.hidden_size = config.hidden_size
-        self.num_heads = config.num_attention_heads
-        self.head_dim = self.hidden_size // self.num_heads
-        self.num_key_value_heads = config.num_key_value_heads
-        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
-        self.max_position_embeddings = config.max_position_embeddings
-        self.rope_theta = config.rope_theta
-        self.is_causal = True
-
-        if (self.head_dim * self.num_heads) != self.hidden_size:
-            raise ValueError(
-                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
-                f" and `num_heads`: {self.num_heads})."
-            )
-
-        self.wqkv = nn.Linear(
-            self.hidden_size,
-            (self.num_heads + 2 * self.num_key_value_heads) * self.head_dim,
-            bias=config.bias,
-        )
-        self.wo = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.bias)
-
-        self._init_rope()
-
-    def _init_rope(self):
-        if self.config.rope_scaling is None:
-            self.rotary_emb = InternLM2RotaryEmbedding(
-                self.head_dim,
-                max_position_embeddings=self.max_position_embeddings,
-                base=self.rope_theta,
-            )
-        else:
-            scaling_type = self.config.rope_scaling["type"]
-            scaling_factor = self.config.rope_scaling["factor"]
-            if scaling_type == "linear":
-                self.rotary_emb = InternLM2LinearScalingRotaryEmbedding(
-                    self.head_dim,
-                    max_position_embeddings=self.max_position_embeddings,
-                    scaling_factor=scaling_factor,
-                    base=self.rope_theta,
-                )
-            elif scaling_type == "dynamic":
-                self.rotary_emb = InternLM2DynamicNTKScalingRotaryEmbedding(
-                    self.head_dim,
-                    max_position_embeddings=self.max_position_embeddings,
-                    scaling_factor=scaling_factor,
-                    base=self.rope_theta,
-                )
-            else:
-                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Cache] = None,
-        output_attentions: bool = False,
-        use_cache: bool = False,  # pylint: disable=unused-argument
-        cache_position: Optional[torch.LongTensor] = None,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        bsz, q_len, _ = hidden_states.size()
-
-        if self.config.pretraining_tp > 1:
-            # split qkv_states by tp size
-            key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
-            qkv_slices = self.wqkv.weight.split(key_value_slicing, dim=0)
-            qkv_states = torch.cat(
-                [F.linear(hidden_states, qkv_slice) for qkv_slice in qkv_slices], dim=-1  # pylint: disable=E1102
-            )
-        else:
-            qkv_states = self.wqkv(hidden_states)
-
-        qkv_states = rearrange(
-            qkv_states,
-            "b q (h gs d) -> b q h gs d",
-            gs=2 + self.num_key_value_groups,
-            d=self.head_dim,
-        )
-
-        query_states = qkv_states[..., : self.num_key_value_groups, :]
-        query_states = rearrange(query_states, "b q h gs d -> b q (h gs) d").transpose(1, 2)
-        key_states = qkv_states[..., -2, :].transpose(1, 2)
-        value_states = qkv_states[..., -1, :].transpose(1, 2)
-
-        cos, sin = self.rotary_emb(value_states, position_ids)
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
-
-        if past_key_value is not None:
-            # sin and cos are specific to RoPE models; cache_position needed for the static cache
-            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
-            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
-
-        key_states = repeat_kv(key_states, self.num_key_value_groups)
-        value_states = repeat_kv(value_states, self.num_key_value_groups)
-
-        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
-
-        if attention_mask is not None:  # no matter the length, we just slice it
-            causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
-            attn_weights = attn_weights + causal_mask
-
-        # upcast attention to fp32
-        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
-        attn_output = torch.matmul(attn_weights, value_states)
-
-        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
-            raise ValueError(
-                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
-                f" {attn_output.size()}"
-            )
-
-        attn_output = attn_output.transpose(1, 2).contiguous()
-
-        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
-
-        if self.config.pretraining_tp > 1:
-            attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
-            o_proj_slices = self.wo.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
-            attn_output = sum(
-                [
-                    F.linear(attn_output[i], o_proj_slices[i])  # pylint: disable=E1102
-                    for i in range(self.config.pretraining_tp)
-                ]
-            )
-        else:
-            attn_output = self.wo(attn_output)
-
-        if not output_attentions:
-            attn_weights = None
-
-        return attn_output, attn_weights, past_key_value
-
-
-class InternLM2FlashAttention2(InternLM2Attention):
-    """
-    InternLM2 flash attention module. This module inherits from `InternLM2Attention` as the weights of the module stays
-    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
-    flash attention and deal with padding tokens in case the input contains any of them.
-    """
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
-        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement,
-        #   that was made default for flash_attn>=2.1. This attribute is used to handle this difference.
-        # Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
-        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1)
-        #   produces a wrong mask (top-left).
-        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.LongTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Cache] = None,
-        output_attentions: bool = False,
-        use_cache: bool = False,
-        cache_position: Optional[torch.LongTensor] = None,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        if isinstance(past_key_value, StaticCache):
-            raise ValueError(
-                "`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` "
-                "make sure to use `sdpa` in the mean time, and open an issue at "
-                "https://github.com/huggingface/transformers"
-            )
-
-        output_attentions = False
-
-        bsz, q_len, _ = hidden_states.size()
-
-        qkv_states = self.wqkv(hidden_states)
-
-        qkv_states = rearrange(
-            qkv_states,
-            "b q (h gs d) -> b q h gs d",
-            gs=2 + self.num_key_value_groups,
-            d=self.head_dim,
-        )
-
-        query_states = qkv_states[..., : self.num_key_value_groups, :]
-        query_states = rearrange(query_states, "b q h gs d -> b q (h gs) d")
-        key_states = qkv_states[..., -2, :]
-        value_states = qkv_states[..., -1, :]
-
-        query_states = query_states.transpose(1, 2)
-        key_states = key_states.transpose(1, 2)
-        value_states = value_states.transpose(1, 2)
-
-        cos, sin = self.rotary_emb(value_states, position_ids)
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
-
-        if past_key_value is not None:
-            # sin and cos are specific to RoPE models; cache_position needed for the static cache
-            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
-            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
-
-        # TODO: These transpose are quite inefficient but Flash Attention requires the layout
-        # [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
-        # to be able to avoid many of these transpose/reshape/view.
-        query_states = query_states.transpose(1, 2)
-        key_states = key_states.transpose(1, 2)
-        value_states = value_states.transpose(1, 2)
-
-        # dropout_rate = self.attention_dropout if self.training else 0.0
-        dropout_rate = 0.0
-
-        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
-        # therefore the input hidden states gets silently casted in float32. Hence, we need
-        # cast them back in the correct dtype just to be sure everything works as expected.
-        # This might slowdown training & inference so it is recommended to not cast the LayerNorms
-        # in fp32. (InternLM2RMSNorm handles it correctly)
-
-        input_dtype = query_states.dtype
-        if input_dtype == torch.float32:
-            if torch.is_autocast_enabled():
-                target_dtype = torch.get_autocast_gpu_dtype()
-            # Handle the case where the model is quantized
-            elif hasattr(self.config, "_pre_quantization_dtype"):
-                target_dtype = self.config._pre_quantization_dtype
-            else:
-                target_dtype = self.wqkv.weight.dtype
-
-            logger.warning_once(
-                f"The input hidden states seems to be silently casted in float32, this might be related to"
-                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
-                f" {target_dtype}."
-            )
-
-            query_states = query_states.to(target_dtype)
-            key_states = key_states.to(target_dtype)
-            value_states = value_states.to(target_dtype)
-
-        attn_output = self._flash_attention_forward(
-            query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
-        )
-
-        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
-        attn_output = self.wo(attn_output)
-
-        if not output_attentions:
-            attn_weights = None
-
-        return attn_output, attn_weights, past_key_value  # pylint: disable=E0606
-
-    def _flash_attention_forward(
-        self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
-    ):
-        """
-        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
-        first unpad the input, then computes the attention scores and pad the final attention scores.
-
-        Args:
-            query_states (`torch.Tensor`):
-                Input query states to be passed to Flash Attention API
-            key_states (`torch.Tensor`):
-                Input key states to be passed to Flash Attention API
-            value_states (`torch.Tensor`):
-                Input value states to be passed to Flash Attention API
-            attention_mask (`torch.Tensor`):
-                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
-                position of padding tokens and 1 for the position of non-padding tokens.
-            dropout (`float`):
-                Attention dropout
-            softmax_scale (`float`, *optional*):
-                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
-        """
-        if not self._flash_attn_uses_top_left_mask:
-            causal = self.is_causal
-        else:
-            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1.
-            # For details, please see the comment in InternLM2FlashAttention2 __init__.
-            causal = self.is_causal and query_length != 1
-
-        # Contains at least one padding token in the sequence
-        if attention_mask is not None:
-            batch_size = query_states.shape[0]
-            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
-                query_states, key_states, value_states, attention_mask, query_length
-            )
-
-            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
-            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
-
-            attn_output_unpad = flash_attn_varlen_func(  # pylint: disable=E0606
-                query_states,
-                key_states,
-                value_states,
-                cu_seqlens_q=cu_seqlens_q,
-                cu_seqlens_k=cu_seqlens_k,
-                max_seqlen_q=max_seqlen_in_batch_q,
-                max_seqlen_k=max_seqlen_in_batch_k,
-                dropout_p=dropout,
-                softmax_scale=softmax_scale,
-                causal=causal,
-            )
-
-            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)  # pylint: disable=E0606
-        else:
-            attn_output = flash_attn_func(  # pylint: disable=E0606
-                query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
-            )
-
-        return attn_output
-
-    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
-        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
-        batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
-
-        key_layer = index_first_axis(  # pylint: disable=E0606
-            key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
-        )
-        value_layer = index_first_axis(  # pylint: disable=E0606
-            value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
-        )
-        if query_length == kv_seq_len:
-            query_layer = index_first_axis(  # pylint: disable=E0606
-                query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
-            )
-            cu_seqlens_q = cu_seqlens_k
-            max_seqlen_in_batch_q = max_seqlen_in_batch_k
-            indices_q = indices_k
-        elif query_length == 1:
-            max_seqlen_in_batch_q = 1
-            cu_seqlens_q = torch.arange(
-                batch_size + 1, dtype=torch.int32, device=query_layer.device
-            )  # There is a memcpy here, that is very bad.
-            indices_q = cu_seqlens_q[:-1]
-            query_layer = query_layer.squeeze(1)
-        else:
-            # The -q_len: slice assumes left padding.
-            attention_mask = attention_mask[:, -query_length:]
-            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(  # pylint: disable=E0606
-                query_layer, attention_mask
-            )
-
-        return (
-            query_layer,
-            key_layer,
-            value_layer,
-            indices_q,
-            (cu_seqlens_q, cu_seqlens_k),
-            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
-        )
-
-
-# Copied from transformers.models.llama.modeling_llama.LllamaSdpaAttention with Llama->InternLM2
-class InternLM2SdpaAttention(InternLM2Attention):
-    """
-    InternLM2 attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
-    `InternLM2Attention` as the weights of the module stays untouched. The only changes are on the forward pass
-    to adapt to SDPA API.
-    """
-
-    # Adapted from InternLM2Attention.forward
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Cache] = None,
-        output_attentions: bool = False,
-        use_cache: bool = False,
-        cache_position: Optional[torch.LongTensor] = None,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        if output_attentions:
-            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"`
-            # once this is implemented.
-            logger.warning_once(
-                "InternLM2Model uses InternLM2SdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` "
-                "does not support `output_attentions=True`. Falling back to the manual attention implementation, "
-                "but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. "
-                'This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
-            )
-            return super().forward(
-                hidden_states=hidden_states,
-                attention_mask=attention_mask,
-                position_ids=position_ids,
-                past_key_value=past_key_value,
-                output_attentions=output_attentions,
-                use_cache=use_cache,
-                cache_position=cache_position,
-            )
-
-        bsz, q_len, _ = hidden_states.size()
-
-        qkv_states = self.wqkv(hidden_states)
-
-        qkv_states = rearrange(
-            qkv_states,
-            "b q (h gs d) -> b q h gs d",
-            gs=2 + self.num_key_value_groups,
-            d=self.head_dim,
-        )
-
-        query_states = qkv_states[..., : self.num_key_value_groups, :]
-        query_states = rearrange(query_states, "b q h gs d -> b q (h gs) d")
-        key_states = qkv_states[..., -2, :]
-        value_states = qkv_states[..., -1, :]
-
-        query_states = query_states.transpose(1, 2)
-        key_states = key_states.transpose(1, 2)
-        value_states = value_states.transpose(1, 2)
-
-        cos, sin = self.rotary_emb(value_states, position_ids)
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
-
-        if past_key_value is not None:
-            # sin and cos are specific to RoPE models; cache_position needed for the static cache
-            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
-            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
-
-        key_states = repeat_kv(key_states, self.num_key_value_groups)
-        value_states = repeat_kv(value_states, self.num_key_value_groups)
-
-        causal_mask = attention_mask
-        if attention_mask is not None:
-            causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
-
-        # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with
-        # custom attn_mask, Reference: https://github.com/pytorch/pytorch/issues/112577.
-        if query_states.device.type == "cuda" and causal_mask is not None:
-            query_states = query_states.contiguous()
-            key_states = key_states.contiguous()
-            value_states = value_states.contiguous()
-
-        # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of
-        # an inline conditional assignment in SDPA to support both torch.compile's dynamic shapes and full graph
-        # options. An inline conditional prevents dynamic shapes from compiling.
-        is_causal = bool(causal_mask is None and q_len > 1)
-
-        attn_output = torch.nn.functional.scaled_dot_product_attention(  # pylint: disable=E1102
-            query_states,
-            key_states,
-            value_states,
-            attn_mask=causal_mask,
-            dropout_p=0.0,
-            is_causal=is_causal,
-        )
-
-        attn_output = attn_output.transpose(1, 2).contiguous()
-        attn_output = attn_output.view(bsz, q_len, self.hidden_size)
-
-        attn_output = self.wo(attn_output)
-
-        return attn_output, None, past_key_value
-
-
-INTERNLM2_ATTENTION_CLASSES = {
-    "eager": InternLM2Attention,
-    "flash_attention_2": InternLM2FlashAttention2,
-    "sdpa": InternLM2SdpaAttention,
-}
-
-
-# Modified from transformers.models.llama.modeling_llama.LlamaDecoderLayer with Llama->InternLM2
-class InternLM2DecoderLayer(nn.Module):
-    """InternLM2 Decoder Layer. This module is a single layer of the InternLM2 model."""
-
-    def __init__(self, config: InternLM2Config, layer_idx: int):
-        super().__init__()
-        self.hidden_size = config.hidden_size
-        self.layer_idx = layer_idx
-
-        self.attention = INTERNLM2_ATTENTION_CLASSES[config.attn_implementation](config=config, layer_idx=layer_idx)
-
-        self.feed_forward = InternLM2MLP(config)
-        self.attention_norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.ffn_norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Cache] = None,
-        output_attentions: Optional[bool] = False,
-        use_cache: Optional[bool] = False,
-        cache_position: Optional[torch.LongTensor] = None,
-    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
-        """
-        Args:
-            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
-            attention_mask (`torch.FloatTensor`, *optional*):
-                attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
-                query_sequence_length, key_sequence_length)` if default attention is used.
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-            use_cache (`bool`, *optional*):
-                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
-                (see `past_key_values`).
-            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
-        """
-        residual = hidden_states
-
-        hidden_states = self.attention_norm(hidden_states)
-
-        # Self Attention
-        hidden_states, self_attn_weights, present_key_value = self.attention(
-            hidden_states=hidden_states,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_value=past_key_value,
-            output_attentions=output_attentions,
-            use_cache=use_cache,
-            cache_position=cache_position,
-        )
-        hidden_states = residual + hidden_states
-
-        # Fully Connected
-        residual = hidden_states
-        hidden_states = self.ffn_norm(hidden_states)
-        hidden_states = self.feed_forward(hidden_states)
-        hidden_states = residual + hidden_states
-
-        outputs = (hidden_states,)
-
-        if output_attentions:
-            outputs += (self_attn_weights,)
-
-        if use_cache:
-            outputs += (present_key_value,)
-
-        return outputs
-
-
-InternLM2_START_DOCSTRING = r"""
-    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
-    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
-    etc.)
-
-    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
-    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
-    and behavior.
-
-    Parameters:
-        config ([`InternLM2Config`]):
-            Model configuration class with all the parameters of the model. Initializing with a config file does not
-            load the weights associated with the model, only the configuration. Check out the
-            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-
-# Copied from transformers.models.llama.modeling_llama.LlamaPreTrainedModel with Llama->InternLM2
-@add_start_docstrings(
-    "The bare InternLM2 Model outputting raw hidden-states without any specific head on top.",
-    InternLM2_START_DOCSTRING,
-)
-class InternLM2PreTrainedModel(PreTrainedModel):
-    """
-    InternLM2 pretraiend model's base class.
-    """
-
-    config_class = InternLM2Config
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["InternLM2DecoderLayer"]
-    _skip_keys_device_placement = ["past_key_values"]
-    _supports_flash_attn_2 = True
-    _supports_sdpa = True
-    _supports_cache_class = True
-    _supports_quantized_cache = True
-    _supports_static_cache = True
-
-    def _init_weights(self, module):
-        std = self.config.initializer_range
-        if isinstance(module, nn.Linear):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-
-
-InternLM2_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
-            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
-            it.
-
-            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            [What are input IDs?](../glossary#input-ids)
-        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-
-            [What are attention masks?](../glossary#attention-mask)
-
-            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
-            `past_key_values`).
-
-            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
-            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
-            information on the default strategy.
-
-            - 1 indicates the head is **not masked**,
-            - 0 indicates the head is **masked**.
-        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
-            config.n_positions - 1]`.
-
-            [What are position IDs?](../glossary#position-ids)
-        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
-            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
-            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
-            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
-
-            Two formats are allowed:
-            - a [`~cache_utils.Cache`] instance;
-            - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
-            shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
-            cache format.
-
-            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
-            legacy cache format will be returned.
-
-            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
-            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
-            of shape `(batch_size, sequence_length)`.
-        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
-            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
-            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
-            model's internal embedding lookup matrix.
-        use_cache (`bool`, *optional*):
-            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
-            `past_key_values`).
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-        cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
-            Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
-            this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
-            the complete sequence length.
-"""
-
-
-# Modified from transformers.models.llama.modeling_llama.LlamaModel with Llama->InternLM2
-@add_start_docstrings(
-    "The bare InternLM2 Model outputting raw hidden-states without any specific head on top.",
-    InternLM2_START_DOCSTRING,
-)
-class InternLM2Model(InternLM2PreTrainedModel):
-    """
-    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`InternLM2DecoderLayer`]
-
-    Args:
-        config: InternLM2Config
-    """
-
-    _auto_class = "AutoModel"
-
-    def __init__(self, config: InternLM2Config):
-        super().__init__(config)
-        self.padding_idx = config.pad_token_id
-        self.vocab_size = config.vocab_size
-        self.config = config
-
-        self.tok_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
-
-        self.layers = nn.ModuleList(
-            [InternLM2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
-        )
-        self.norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-
-        self.gradient_checkpointing = False
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.tok_embeddings
-
-    def set_input_embeddings(self, value):
-        self.tok_embeddings = value
-
-    @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-    ) -> Union[Tuple, BaseModelOutputWithPast]:
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
-
-        if self.gradient_checkpointing and self.training and use_cache:
-            logger.warning_once(
-                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
-            )
-            use_cache = False
-
-        if inputs_embeds is None:
-            inputs_embeds = self.tok_embeddings(input_ids)
-
-        return_legacy_cache = False
-        if use_cache and not isinstance(past_key_values, Cache):  # kept for BC (non `Cache` `past_key_values` inputs)
-            return_legacy_cache = True
-            past_key_values = DynamicCache.from_legacy_cache(past_key_values)
-
-        if cache_position is None:
-            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
-            cache_position = torch.arange(
-                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
-            )
-        if position_ids is None:
-            position_ids = cache_position.unsqueeze(0)
-
-        causal_mask = self._update_causal_mask(
-            attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
-        )
-
-        # embed positions
-        hidden_states = inputs_embeds
-
-        # decoder layers
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attns = () if output_attentions else None
-        next_decoder_cache = None
-
-        for decoder_layer in self.layers:
-            if output_hidden_states:
-                all_hidden_states += (hidden_states,)
-
-            if self.gradient_checkpointing and self.training:
-                layer_outputs = self._gradient_checkpointing_func(
-                    decoder_layer.__call__,
-                    hidden_states,
-                    causal_mask,
-                    position_ids,
-                    past_key_values,
-                    output_attentions,
-                    use_cache,
-                    cache_position,
-                )
-            else:
-                layer_outputs = decoder_layer(
-                    hidden_states,
-                    attention_mask=causal_mask,
-                    position_ids=position_ids,
-                    past_key_value=past_key_values,
-                    output_attentions=output_attentions,
-                    use_cache=use_cache,
-                    cache_position=cache_position,
-                )
-
-            hidden_states = layer_outputs[0]
-
-            if use_cache:
-                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
-
-            if output_attentions:
-                all_self_attns += (layer_outputs[1],)
-
-        hidden_states = self.norm(hidden_states)
-
-        # add hidden states from the last decoder layer
-        if output_hidden_states:
-            all_hidden_states += (hidden_states,)
-
-        next_cache = next_decoder_cache if use_cache else None
-        if return_legacy_cache:
-            next_cache = next_cache.to_legacy_cache()
-
-        if not return_dict:
-            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
-        return BaseModelOutputWithPast(
-            last_hidden_state=hidden_states,
-            past_key_values=next_cache,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attns,
-        )
-
-    def _update_causal_mask(
-        self,
-        attention_mask: torch.Tensor,
-        input_tensor: torch.Tensor,
-        cache_position: torch.Tensor,
-        past_key_values: Cache,
-        output_attentions: bool,
-    ):
-        # TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length
-        # even when the static KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at
-        # each decode steps due to the dynamic shapes. (`recording cudagraph tree for symint key 13`, etc.), which is
-        # VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using `fullgraph=True`.
-        # See more context in https://github.com/huggingface/transformers/pull/29114
-
-        if self.config.attn_implementation == "flash_attention_2":
-            if attention_mask is not None and 0.0 in attention_mask:
-                return attention_mask
-            return None
-
-        # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
-        # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
-        # to infer the attention mask.
-        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
-        using_static_cache = isinstance(past_key_values, StaticCache)
-
-        # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
-        if self.config.attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
-            if AttentionMaskConverter._ignore_causal_mask_sdpa(
-                attention_mask,
-                inputs_embeds=input_tensor,
-                past_key_values_length=past_seen_tokens,
-                is_training=self.training,
-            ):
-                return None
-
-        dtype, device = input_tensor.dtype, input_tensor.device
-        min_dtype = torch.finfo(dtype).min
-        sequence_length = input_tensor.shape[1]
-        if using_static_cache:
-            target_length = past_key_values.get_max_length()
-        else:
-            target_length = (
-                attention_mask.shape[-1]
-                if isinstance(attention_mask, torch.Tensor)
-                else past_seen_tokens + sequence_length + 1
-            )
-
-        if attention_mask is not None and attention_mask.dim() == 4:
-            # in this case we assume that the mask comes already in inverted form and requires no inversion or slicing
-            if attention_mask.max() != 0:
-                raise ValueError("Custom 4D attention mask should be passed in inverted form with max==0`")
-            causal_mask = attention_mask
-        else:
-            causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device)
-            if sequence_length != 1:
-                causal_mask = torch.triu(causal_mask, diagonal=1)
-            causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
-            causal_mask = causal_mask[None, None, :, :].expand(input_tensor.shape[0], 1, -1, -1)
-            if attention_mask is not None:
-                causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
-                mask_length = attention_mask.shape[-1]
-                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
-                padding_mask = padding_mask == 0
-                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
-                    padding_mask, min_dtype
-                )
-        if (
-            self.config.attn_implementation == "sdpa"
-            and attention_mask is not None
-            and attention_mask.device.type == "cuda"
-            and not output_attentions
-        ):
-            # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
-            # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
-            # Details: https://github.com/pytorch/pytorch/issues/110213
-            causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)  # pylint: disable=E1120
-
-        return causal_mask
-
-
-# Modified from transformers.models.llama.modeling_llama.LlamaForCausalLM
-class InternLM2ForCausalLM(InternLM2PreTrainedModel):
-    """Causal language model (CLM) for InternLM2."""
-
-    _auto_class = "AutoModelForCausalLM"
-    _tied_weights_keys = ["output.weight"]
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.model = InternLM2Model(config)
-        self.vocab_size = config.vocab_size
-        self.output = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.model.tok_embeddings
-
-    def set_input_embeddings(self, value):
-        self.model.tok_embeddings = value
-
-    def get_output_embeddings(self):
-        return self.output
-
-    def set_output_embeddings(self, new_embeddings):
-        self.output = new_embeddings
-
-    def set_decoder(self, decoder):
-        self.model = decoder
-
-    def get_decoder(self):
-        return self.model
-
-    @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-    ) -> Union[Tuple, CausalLMOutputWithPast]:
-        r"""
-        Args:
-            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
-                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-
-        Returns:
-
-        Example:
-
-        ```python
-        >>> from transformers import AutoTokenizer, InternLM2ForCausalLM
-
-        >>> model = InternLM2ForCausalLM.from_pretrained("meta-InternLM2/InternLM2-2-7b-hf")
-        >>> tokenizer = AutoTokenizer.from_pretrained("meta-InternLM2/InternLM2-2-7b-hf")
-
-        >>> prompt = "Hey, are you conscious? Can you talk to me?"
-        >>> inputs = tokenizer(prompt, return_tensors="pt")
-
-        >>> # Generate
-        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
-        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
-        ```"""
-
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-        outputs = self.model(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            cache_position=cache_position,
-        )
-
-        hidden_states = outputs[0]
-        if self.config.pretraining_tp > 1:
-            output_slices = self.output.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
-            logits = [
-                F.linear(hidden_states, output_slices[i])  # pylint: disable=not-callable
-                for i in range(self.config.pretraining_tp)
-            ]
-            logits = torch.cat(logits, dim=-1)
-        else:
-            logits = self.output(hidden_states)
-        logits = logits.float()
-
-        loss = None
-        if labels is not None:
-            # Shift so that tokens < n predict n
-            shift_logits = logits[..., :-1, :].contiguous()
-            shift_labels = labels[..., 1:].contiguous()
-            # Flatten the tokens
-            loss_fct = CrossEntropyLoss()
-            shift_logits = shift_logits.view(-1, self.config.vocab_size)
-            shift_labels = shift_labels.view(-1)
-            # Enable model parallelism
-            shift_labels = shift_labels.to(shift_logits.device)
-            loss = loss_fct(shift_logits, shift_labels)
-
-        if not return_dict:
-            output = (logits,) + outputs[1:]
-            return (loss,) + output if loss is not None else output
-
-        return CausalLMOutputWithPast(
-            loss=loss,
-            logits=logits,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-    def prepare_inputs_for_generation(
-        self,
-        input_ids,
-        past_key_values=None,
-        attention_mask=None,
-        inputs_embeds=None,
-        cache_position=None,
-        use_cache=True,
-        **kwargs,
-    ):
-        past_length = 0
-        if past_key_values is not None:
-            if isinstance(past_key_values, Cache):
-                past_length = cache_position[0] if cache_position is not None else past_key_values.get_seq_length()
-                max_cache_length = (
-                    torch.tensor(past_key_values.get_max_length(), device=input_ids.device)
-                    if past_key_values.get_max_length() is not None
-                    else None
-                )
-                cache_length = past_length if max_cache_length is None else torch.min(max_cache_length, past_length)
-            # TODO joao: remove this `else` after `generate` prioritizes `Cache` objects
-            else:
-                cache_length = past_length = past_key_values[0][0].shape[2]
-                max_cache_length = None
-
-            # Keep only the unprocessed tokens:
-            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
-            # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as input)
-            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
-                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
-            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
-            # input_ids based on the past_length.
-            elif past_length < input_ids.shape[1]:
-                input_ids = input_ids[:, past_length:]
-            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
-
-            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
-            if (
-                max_cache_length is not None
-                and attention_mask is not None
-                and cache_length + input_ids.shape[1] > max_cache_length
-            ):
-                attention_mask = attention_mask[:, -max_cache_length:]  # pylint: disable=E1130
-
-        position_ids = kwargs.get("position_ids", None)
-        if attention_mask is not None and position_ids is None:
-            # create position_ids on the fly for batch generation
-            position_ids = attention_mask.long().cumsum(-1) - 1
-            position_ids.masked_fill_(attention_mask == 0, 1)
-            if past_key_values:
-                position_ids = position_ids[:, -input_ids.shape[1] :]
-
-        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
-        if inputs_embeds is not None and past_key_values is None:
-            model_inputs = {"inputs_embeds": inputs_embeds}
-        else:
-            # The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise
-            # recompiles graphs as the stride of the inputs is a guard.
-            # Ref: https://github.com/huggingface/transformers/pull/29114
-            # TODO: use `next_tokens` directly instead.
-            model_inputs = {"input_ids": input_ids.contiguous()}
-
-        input_length = position_ids.shape[-1] if position_ids is not None else input_ids.shape[-1]
-        if cache_position is None:
-            cache_position = torch.arange(past_length, past_length + input_length, device=input_ids.device)
-        elif use_cache:
-            cache_position = cache_position[-input_length:]
-
-        model_inputs.update(
-            {
-                "position_ids": position_ids,
-                "cache_position": cache_position,
-                "past_key_values": past_key_values,
-                "use_cache": use_cache,
-                "attention_mask": attention_mask,
-            }
-        )
-        return model_inputs
-
-    @staticmethod
-    def _reorder_cache(past_key_values, beam_idx):
-        reordered_past = ()
-        for layer_past in past_key_values:
-            reordered_past += (
-                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
-            )
-        return reordered_past
-
-    def build_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, meta_instruction=""):
-        if history is None:
-            history = []
-        if tokenizer.add_bos_token:
-            prompt = ""
-        else:
-            prompt = tokenizer.bos_token
-        if meta_instruction:
-            prompt += f"""<|im_start|>system\n{meta_instruction}<|im_end|>\n"""
-        for record in history:
-            prompt += f"""<|im_start|>user\n{record[0]}<|im_end|>\n<|im_start|>assistant\n{record[1]}<|im_end|>\n"""
-        prompt += f"""<|im_start|>user\n{query}<|im_end|>\n<|im_start|>assistant\n"""
-        return tokenizer([prompt], return_tensors="pt")
-
-    @torch.no_grad()
-    def chat(
-        self,
-        tokenizer,
-        query: str,
-        history: Optional[List[Tuple[str, str]]] = None,
-        streamer: Optional[BaseStreamer] = None,
-        max_new_tokens: int = 1024,
-        do_sample: bool = True,
-        temperature: float = 0.8,
-        top_p: float = 0.8,
-        meta_instruction: str = "You are an AI assistant whose name is InternLM (书生·浦语).\n"
-        "- InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory "
-        "(上海人工智能实验室). It is designed to be helpful, honest, and harmless.\n"
-        "- InternLM (书生·浦语) can understand and communicate fluently in the language chosen by the user such "
-        "as English and 中文.",
-        **kwargs,
-    ):
-        if history is None:
-            history = []
-        inputs = self.build_inputs(tokenizer, query, history, meta_instruction)
-        inputs = {k: v.to(self.device) for k, v in inputs.items() if torch.is_tensor(v)}
-        # also add end-of-assistant token in eos token id to avoid unnecessary generation
-        eos_token_id = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids(["<|im_end|>"])[0]]
-        outputs = self.generate(
-            **inputs,
-            streamer=streamer,
-            max_new_tokens=max_new_tokens,
-            do_sample=do_sample,
-            temperature=temperature,
-            top_p=top_p,
-            eos_token_id=eos_token_id,
-            **kwargs,
-        )
-        outputs = outputs[0].cpu().tolist()[len(inputs["input_ids"][0]) :]
-        response = tokenizer.decode(outputs, skip_special_tokens=True)
-        response = response.split("<|im_end|>")[0]
-        history = history + [(query, response)]
-        return response, history
-
-    @torch.no_grad()
-    def stream_chat(
-        self,
-        tokenizer,
-        query: str,
-        history: List[Tuple[str, str]] = None,
-        max_new_tokens: int = 1024,
-        do_sample: bool = True,
-        temperature: float = 0.8,
-        top_p: float = 0.8,
-        **kwargs,
-    ):
-        if history is None:
-            history = []
-        """
-        Return a generator in format: (response, history)
-        Eg.
-        ('你好，有什么可以帮助您的吗', [('你好', '你好，有什么可以帮助您的吗')])
-        ('你好，有什么可以帮助您的吗？', [('你好', '你好，有什么可以帮助您的吗？')])
-        """
-        if BaseStreamer is None:
-            raise ModuleNotFoundError(
-                "The version of `transformers` is too low. Please make sure "
-                "that you have installed `transformers>=4.28.0`."
-            )
-
-        response_queue = queue.Queue(maxsize=20)
-
-        class ChatStreamer(BaseStreamer):
-            """
-            Streamer used in generate to print words one by one.
-            """
-
-            def __init__(self, tokenizer) -> None:
-                super().__init__()
-                self.tokenizer = tokenizer
-                self.queue = response_queue
-                self.query = query
-                self.history = history
-                self.response = ""
-                self.cache = []
-                self.received_inputs = False
-                self.queue.put((self.response, history + [(self.query, self.response)]))
-
-            def put(self, value):
-                if len(value.shape) > 1 and value.shape[0] > 1:
-                    raise ValueError("ChatStreamer only supports batch size 1")
-                elif len(value.shape) > 1:
-                    value = value[0]
-
-                if not self.received_inputs:
-                    # The first received value is input_ids, ignore here
-                    self.received_inputs = True
-                    return
-
-                self.cache.extend(value.tolist())
-                token = self.tokenizer.decode(self.cache, skip_special_tokens=True)
-                if token.strip() != "<|im_end|>":
-                    self.response = self.response + token
-                    history = self.history + [(self.query, self.response)]
-                    self.queue.put((self.response, history))
-                    self.cache = []
-                else:
-                    self.end()
-
-            def end(self):
-                self.queue.put(None)
-
-        def stream_producer():
-            return self.chat(
-                tokenizer=tokenizer,
-                query=query,
-                streamer=ChatStreamer(tokenizer=tokenizer),
-                history=history,
-                max_new_tokens=max_new_tokens,
-                do_sample=do_sample,
-                temperature=temperature,
-                top_p=top_p,
-                **kwargs,
-            )
-
-        def consumer():
-            producer = threading.Thread(target=stream_producer)
-            producer.start()
-            while True:
-                res = response_queue.get()
-                if res is None:
-                    return
-                yield res
-
-        return consumer()
-
-
-# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with Llama->InternLM2
-@add_start_docstrings(
-    """
-    The InternLM2 Model transformer with a sequence classification head on top (linear layer).
-
-    [`InternLM2ForSequenceClassification`] uses the last token in order to do the classification, as other causal models
-    (e.g. GPT-2) do.
-
-    Since it does classification on the last token, it requires to know the position of the last token. If a
-    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
-    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
-    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
-    each row of the batch).
-    """,
-    InternLM2_START_DOCSTRING,
-)
-class InternLM2ForSequenceClassification(InternLM2PreTrainedModel):
-    """Sequence Classification Head for InternLM2 Model."""
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.model = InternLM2Model(config)
-        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.model.tok_embeddings
-
-    def set_input_embeddings(self, value):
-        self.model.tok_embeddings = value
-
-    @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
-            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
-            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        transformer_outputs = self.model(
-            input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        hidden_states = transformer_outputs[0]
-        logits = self.score(hidden_states)
-
-        if input_ids is not None:
-            batch_size = input_ids.shape[0]
-        else:
-            batch_size = inputs_embeds.shape[0]
-
-        if self.config.pad_token_id is None and batch_size != 1:
-            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
-        if self.config.pad_token_id is None:
-            sequence_lengths = -1
-        else:
-            if input_ids is not None:
-                # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
-                sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
-                sequence_lengths = sequence_lengths % input_ids.shape[-1]
-                sequence_lengths = sequence_lengths.to(logits.device)
-            else:
-                sequence_lengths = -1
-
-        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
-
-        loss = None
-        if labels is not None:
-            labels = labels.to(logits.device)
-            if self.config.problem_type is None:
-                if self.num_labels == 1:
-                    self.config.problem_type = "regression"
-                elif self.num_labels > 1 and (labels.dtype in (torch.long, torch.int)):
-                    self.config.problem_type = "single_label_classification"
-                else:
-                    self.config.problem_type = "multi_label_classification"
-
-            if self.config.problem_type == "regression":
-                loss_fct = MSELoss()
-                if self.num_labels == 1:
-                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
-                else:
-                    loss = loss_fct(pooled_logits, labels)
-            elif self.config.problem_type == "single_label_classification":
-                loss_fct = CrossEntropyLoss()
-                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
-            elif self.config.problem_type == "multi_label_classification":
-                loss_fct = BCEWithLogitsLoss()
-                loss = loss_fct(pooled_logits, labels)
-        if not return_dict:
-            output = (pooled_logits,) + transformer_outputs[1:]
-            return ((loss,) + output) if loss is not None else output
-
-        return SequenceClassifierOutputWithPast(
-            loss=loss,
-            logits=pooled_logits,
-            past_key_values=transformer_outputs.past_key_values,
-            hidden_states=transformer_outputs.hidden_states,
-            attentions=transformer_outputs.attentions,
-        )
-
-
-# Copied from transformers.models.llama.modeling_llama.LlamaForQuestionAnswering with Llama->InternLM2
-@add_start_docstrings(
-    """
-The InternLM2 Model transformer with a span classification head on top for extractive question-answering tasks like
-SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
-    """,
-    InternLM2_START_DOCSTRING,
-)
-class InternLM2ForQuestionAnswering(InternLM2PreTrainedModel):
-    """Question Answering model for InternLM2."""
-
-    base_model_prefix = "transformer"
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.transformer = InternLM2Model(config)
-        self.qa_outputs = nn.Linear(config.hidden_size, 2)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.transformer.tok_embeddings
-
-    def set_input_embeddings(self, value):
-        self.transformer.tok_embeddings = value
-
-    @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        start_positions: Optional[torch.LongTensor] = None,
-        end_positions: Optional[torch.LongTensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, QuestionAnsweringModelOutput]:
-        r"""
-        start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for position (index) of the start of the labelled span for computing the token classification loss.
-            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
-            are not taken into account for computing the loss.
-        end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for position (index) of the end of the labelled span for computing the token classification loss.
-            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
-            are not taken into account for computing the loss.
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.transformer(
-            input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        sequence_output = outputs[0]
-
-        logits = self.qa_outputs(sequence_output)
-        start_logits, end_logits = logits.split(1, dim=-1)
-        start_logits = start_logits.squeeze(-1).contiguous()
-        end_logits = end_logits.squeeze(-1).contiguous()
-
-        total_loss = None
-        if start_positions is not None and end_positions is not None:
-            # If we are on multi-GPU, split add a dimension
-            if len(start_positions.size()) > 1:
-                start_positions = start_positions.squeeze(-1).to(start_logits.device)
-            if len(end_positions.size()) > 1:
-                end_positions = end_positions.squeeze(-1).to(end_logits.device)
-            # sometimes the start/end positions are outside our model inputs, we ignore these terms
-            ignored_index = start_logits.size(1)
-            start_positions = start_positions.clamp(0, ignored_index)
-            end_positions = end_positions.clamp(0, ignored_index)
-
-            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
-            start_loss = loss_fct(start_logits, start_positions)
-            end_loss = loss_fct(end_logits, end_positions)
-            total_loss = (start_loss + end_loss) / 2
-
-        if not return_dict:
-            output = (start_logits, end_logits) + outputs[2:]
-            return ((total_loss,) + output) if total_loss is not None else output
-
-        return QuestionAnsweringModelOutput(
-            loss=total_loss,
-            start_logits=start_logits,
-            end_logits=end_logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-# Copied from transformers.models.llama.modeling_llama.LlamaForTokenClassification with Llama->InternLM2
-@add_start_docstrings(
-    """
-    The InternLM2 Model transformer with a token classification head on top (a linear layer on top of the hidden-states
-    output) e.g. for Named-Entity-Recognition (NER) tasks.
-    """,
-    InternLM2_START_DOCSTRING,
-)
-class InternLM2ForTokenClassification(InternLM2PreTrainedModel):
-    """Token classification model for InternLM2."""
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.model = InternLM2Model(config)
-        if getattr(config, "classifier_dropout", None) is not None:
-            classifier_dropout = config.classifier_dropout
-        elif getattr(config, "hidden_dropout", None) is not None:
-            classifier_dropout = config.hidden_dropout
-        else:
-            classifier_dropout = 0.1
-        self.dropout = nn.Dropout(classifier_dropout)
-        self.score = nn.Linear(config.hidden_size, config.num_labels)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.model.tok_embeddings
-
-    def set_input_embeddings(self, value):
-        self.model.tok_embeddings = value
-
-    @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
-            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
-            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.model(
-            input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        sequence_output = outputs[0]
-        sequence_output = self.dropout(sequence_output)
-        logits = self.score(sequence_output)
-
-        loss = None
-        if labels is not None:
-            loss_fct = CrossEntropyLoss()
-            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
-
-        if not return_dict:
-            output = (logits,) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
-        return TokenClassifierOutput(
-            loss=loss,
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )

llama-factory/merged_models/internlm2_5-7b-chat-1m_sft_bf16_p2_full/tokenization_internlm2.py

@@ -1,236 +0,0 @@
-# coding=utf-8
-# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
-#
-# This code is based on transformers/src/transformers/models/llama/tokenization_llama.py
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Tokenization classes for InternLM."""
-import os
-from shutil import copyfile
-from typing import Any, Dict, List, Optional, Tuple
-
-import sentencepiece as spm
-from transformers.tokenization_utils import PreTrainedTokenizer
-from transformers.utils import logging
-
-logger = logging.get_logger(__name__)
-
-VOCAB_FILES_NAMES = {"vocab_file": "./tokenizer.model"}
-
-PRETRAINED_VOCAB_FILES_MAP = {}
-
-
-# Modified from transformers.model.llama.tokenization_llama.LlamaTokenizer
-class InternLM2Tokenizer(PreTrainedTokenizer):
-    """
-    Construct a InternLM2 tokenizer. Based on byte-level Byte-Pair-Encoding.
-
-    Args:
-        vocab_file (`str`):
-            Path to the vocabulary file.
-    """
-
-    vocab_files_names = VOCAB_FILES_NAMES
-    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
-    model_input_names = ["input_ids", "attention_mask"]
-    _auto_class = "AutoTokenizer"
-
-    def __init__(
-        self,
-        vocab_file,
-        unk_token="<unk>",
-        bos_token="<s>",
-        eos_token="</s>",
-        pad_token="</s>",
-        sp_model_kwargs: Optional[Dict[str, Any]] = None,
-        add_bos_token=True,
-        add_eos_token=False,
-        decode_with_prefix_space=False,
-        clean_up_tokenization_spaces=False,
-        **kwargs,
-    ):
-        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
-        self.vocab_file = vocab_file
-        self.add_bos_token = add_bos_token
-        self.add_eos_token = add_eos_token
-        self.decode_with_prefix_space = decode_with_prefix_space
-        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
-        self.sp_model.Load(vocab_file)
-        self._no_prefix_space_tokens = None
-        super().__init__(
-            bos_token=bos_token,
-            eos_token=eos_token,
-            unk_token=unk_token,
-            pad_token=pad_token,
-            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
-            **kwargs,
-        )
-
-    @property
-    def no_prefix_space_tokens(self):
-        if self._no_prefix_space_tokens is None:
-            vocab = self.convert_ids_to_tokens(list(range(self.vocab_size)))
-            self._no_prefix_space_tokens = {i for i, tok in enumerate(vocab) if not tok.startswith("▁")}
-        return self._no_prefix_space_tokens
-
-    @property
-    def vocab_size(self):
-        """Returns vocab size"""
-        return self.sp_model.get_piece_size()
-
-    @property
-    def bos_token_id(self) -> Optional[int]:
-        return self.sp_model.bos_id()
-
-    @property
-    def eos_token_id(self) -> Optional[int]:
-        return self.sp_model.eos_id()
-
-    def get_vocab(self):
-        """Returns vocab as a dict"""
-        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
-        vocab.update(self.added_tokens_encoder)
-        return vocab
-
-    def _tokenize(self, text):
-        """Returns a tokenized string."""
-        return self.sp_model.encode(text, out_type=str)
-
-    def _convert_token_to_id(self, token):
-        """Converts a token (str) in an id using the vocab."""
-        return self.sp_model.piece_to_id(token)
-
-    def _convert_id_to_token(self, index):
-        """Converts an index (integer) in a token (str) using the vocab."""
-        token = self.sp_model.IdToPiece(index)
-        return token
-
-    def _maybe_add_prefix_space(self, tokens, decoded):
-        if tokens and tokens[0] not in self.no_prefix_space_tokens:
-            return " " + decoded
-        else:
-            return decoded
-
-    def convert_tokens_to_string(self, tokens):
-        """Converts a sequence of tokens (string) in a single string."""
-        current_sub_tokens = []
-        out_string = ""
-        prev_is_special = False
-        for token in tokens:
-            # make sure that special tokens are not decoded using sentencepiece model
-            if token in self.all_special_tokens:
-                if not prev_is_special:
-                    out_string += " "
-                out_string += self.sp_model.decode(current_sub_tokens) + token
-                prev_is_special = True
-                current_sub_tokens = []
-            else:
-                current_sub_tokens.append(token)
-                prev_is_special = False
-        out_string += self.sp_model.decode(current_sub_tokens)
-        out_string = self.clean_up_tokenization(out_string)
-        out_string = self._maybe_add_prefix_space(tokens=tokens, decoded=out_string)
-        return out_string[1:]
-
-    def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
-        """
-        Save the vocabulary and special tokens file to a directory.
-
-        Args:
-            save_directory (`str`):
-                The directory in which to save the vocabulary.
-
-        Returns:
-            `Tuple(str)`: Paths to the files saved.
-        """
-        if not os.path.isdir(save_directory):
-            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
-            return
-        out_vocab_file = os.path.join(
-            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
-        )
-
-        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
-            copyfile(self.vocab_file, out_vocab_file)
-        elif not os.path.isfile(self.vocab_file):
-            with open(out_vocab_file, "wb") as fi:
-                content_spiece_model = self.sp_model.serialized_model_proto()
-                fi.write(content_spiece_model)
-
-        return (out_vocab_file,)
-
-    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
-        if self.add_bos_token:
-            bos_token_ids = [self.bos_token_id]
-        else:
-            bos_token_ids = []
-
-        output = bos_token_ids + token_ids_0
-
-        if token_ids_1 is not None:
-            output = output + token_ids_1
-
-        if self.add_eos_token:
-            output = output + [self.eos_token_id]
-
-        return output
-
-    def get_special_tokens_mask(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
-    ) -> List[int]:
-        """
-        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
-        special tokens using the tokenizer `prepare_for_model` method.
-
-        Args:
-            token_ids_0 (`List[int]`):
-                List of IDs.
-            token_ids_1 (`List[int]`, *optional*):
-                Optional second list of IDs for sequence pairs.
-            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
-                Whether or not the token list is already formatted with special tokens for the model.
-
-        Returns:
-            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
-        """
-        if already_has_special_tokens:
-            return super().get_special_tokens_mask(
-                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
-            )
-
-        if token_ids_1 is None:
-            return [1] + ([0] * len(token_ids_0)) + [1]
-        return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
-
-    def create_token_type_ids_from_sequences(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
-    ) -> List[int]:
-        """
-        Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
-        use of token type ids, therefore a list of zeros is returned.
-
-        Args:
-            token_ids_0 (`List[int]`):
-                List of IDs.
-            token_ids_1 (`List[int]`, *optional*):
-                Optional second list of IDs for sequence pairs.
-
-        Returns:
-            `List[int]`: List of zeros.
-        """
-        eos = [self.eos_token_id]
-
-        if token_ids_1 is None:
-            return len(token_ids_0 + eos) * [0]
-        return len(token_ids_0 + eos + token_ids_1 + eos) * [0]

llama-factory/merged_models/internlm2_5-7b-chat-1m_sft_bf16_p2_full/tokenization_internlm2_fast.py

@@ -1,214 +0,0 @@
-# coding=utf-8
-# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
-#
-# This code is based on transformers/src/transformers/models/llama/tokenization_llama_fast.py
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Tokenization Fast class for InternLM."""
-import os
-from shutil import copyfile
-from typing import Any, Dict, Optional, Tuple
-
-from tokenizers import processors, decoders, Tokenizer, normalizers
-from tokenizers.models import BPE
-
-from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
-from transformers.utils import logging
-
-from transformers.convert_slow_tokenizer import (
-    SLOW_TO_FAST_CONVERTERS,
-    SpmConverter,
-    SentencePieceExtractor,
-)
-
-from .tokenization_internlm2 import InternLM2Tokenizer
-
-logger = logging.get_logger(__name__)
-
-VOCAB_FILES_NAMES = {"vocab_file": "./tokenizer.model"}
-
-# Modified from transformers.convert_slow_tokenizer.LlamaConverter
-class InternLM2Converter(SpmConverter):
-    handle_byte_fallback = True
-
-    def vocab(self, proto):
-        vocab = [
-            ("<unk>", 0.0),
-            ("<s>", 0.0),
-            ("</s>", 0.0),
-        ]
-        vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]]
-        return vocab
-
-    def unk_id(self, proto):
-        unk_id = 0
-        return unk_id
-
-    def decoder(self, replacement, add_prefix_space):
-        decoders_sequence = [
-            decoders.Replace("▁", " "),
-            decoders.ByteFallback(),
-            decoders.Fuse(),
-        ]
-        if self.proto.normalizer_spec.add_dummy_prefix:
-            decoders_sequence.append(decoders.Strip(content=" ", left=1))
-        return decoders.Sequence(decoders_sequence)
-
-    def tokenizer(self, proto):
-        model_type = proto.trainer_spec.model_type
-        vocab_scores = self.vocab(proto)
-        # special tokens
-        added_tokens = self.original_tokenizer.added_tokens_decoder
-        for i in range(len(vocab_scores)):
-            piece, score = vocab_scores[i]
-            if i in added_tokens:
-                vocab_scores[i] = (added_tokens[i].content, score)
-        if model_type == 1:
-            raise RuntimeError("InternLM2 is supposed to be a BPE model!")
-
-        elif model_type == 2:
-            _, merges = SentencePieceExtractor(self.original_tokenizer.vocab_file).extract(vocab_scores)
-            bpe_vocab = {word: i for i, (word, _score) in enumerate(vocab_scores)}
-            tokenizer = Tokenizer(
-                BPE(bpe_vocab, merges, unk_token=proto.trainer_spec.unk_piece, fuse_unk=True, byte_fallback=True)
-            )
-            tokenizer.add_special_tokens(
-                [ added_token for index, added_token in added_tokens.items()]
-            )
-        else:
-            raise Exception(
-                "You're trying to run a `Unigram` model but you're file was trained with a different algorithm"
-            )
-
-        return tokenizer
-
-    def normalizer(self, proto):
-        normalizers_list = []
-        if proto.normalizer_spec.add_dummy_prefix:
-            normalizers_list.append(normalizers.Prepend(prepend="▁"))
-        normalizers_list.append(normalizers.Replace(pattern=" ", content="▁"))
-        return normalizers.Sequence(normalizers_list)
-
-    def pre_tokenizer(self, replacement, add_prefix_space):
-        return None
-
-SLOW_TO_FAST_CONVERTERS["InternLM2Tokenizer"] = InternLM2Converter
-
-
-# Modified from transformers.model.llama.tokenization_llama_fast.LlamaTokenizerFast -> InternLM2TokenizerFast
-class InternLM2TokenizerFast(PreTrainedTokenizerFast):
-    vocab_files_names = VOCAB_FILES_NAMES
-    slow_tokenizer_class = InternLM2Tokenizer
-    padding_side = "left"
-    model_input_names = ["input_ids", "attention_mask"]
-    _auto_class = "AutoTokenizer"
-
-    def __init__(
-        self,
-        vocab_file,
-        unk_token="<unk>",
-        bos_token="<s>",
-        eos_token="</s>",
-        pad_token="</s>",
-        sp_model_kwargs: Optional[Dict[str, Any]] = None,
-        add_bos_token=True,
-        add_eos_token=False,
-        decode_with_prefix_space=False,
-        clean_up_tokenization_spaces=False,
-        **kwargs,
-    ):
-        super().__init__(
-            vocab_file=vocab_file,
-            unk_token=unk_token,
-            bos_token=bos_token,
-            eos_token=eos_token,
-            pad_token=pad_token,
-            sp_model_kwargs=sp_model_kwargs,
-            add_bos_token=add_bos_token,
-            add_eos_token=add_eos_token,
-            decode_with_prefix_space=decode_with_prefix_space,
-            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
-            **kwargs,
-        )
-        self._add_bos_token = add_bos_token
-        self._add_eos_token = add_eos_token
-        self.update_post_processor()
-        self.vocab_file = vocab_file
-
-    @property
-    def can_save_slow_tokenizer(self) -> bool:
-        return os.path.isfile(self.vocab_file) if self.vocab_file else False
-
-    def update_post_processor(self):
-        """
-        Updates the underlying post processor with the current `bos_token` and `eos_token`.
-        """
-        bos = self.bos_token
-        bos_token_id = self.bos_token_id
-        if bos is None and self.add_bos_token:
-            raise ValueError("add_bos_token = True but bos_token = None")
-
-        eos = self.eos_token
-        eos_token_id = self.eos_token_id
-        if eos is None and self.add_eos_token:
-            raise ValueError("add_eos_token = True but eos_token = None")
-
-        single = f"{(bos+':0 ') if self.add_bos_token else ''}$A:0{(' '+eos+':0') if self.add_eos_token else ''}"
-        pair = f"{single}{(' '+bos+':1') if self.add_bos_token else ''} $B:1{(' '+eos+':1') if self.add_eos_token else ''}"
-
-        special_tokens = []
-        if self.add_bos_token:
-            special_tokens.append((bos, bos_token_id))
-        if self.add_eos_token:
-            special_tokens.append((eos, eos_token_id))
-        self._tokenizer.post_processor = processors.TemplateProcessing(
-            single=single, pair=pair, special_tokens=special_tokens
-        )
-
-    @property
-    def add_eos_token(self):
-        return self._add_eos_token
-
-    @property
-    def add_bos_token(self):
-        return self._add_bos_token
-
-    @add_eos_token.setter
-    def add_eos_token(self, value):
-        self._add_eos_token = value
-        self.update_post_processor()
-
-    @add_bos_token.setter
-    def add_bos_token(self, value):
-        self._add_bos_token = value
-        self.update_post_processor()
-
-    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
-        if not self.can_save_slow_tokenizer:
-            raise ValueError(
-                "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
-                "tokenizer."
-            )
-
-        if not os.path.isdir(save_directory):
-            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
-            return
-        out_vocab_file = os.path.join(
-            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
-        )
-
-        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
-            copyfile(self.vocab_file, out_vocab_file)
-
-        return (out_vocab_file,)

llama-factory/merged_models/internlm2_5-7b-chat-1m_sft_bf16_p2_full/tokenizer_config.json

@@ -1,1640 +0,0 @@
-{
-  "add_bos_token": true,
-  "add_eos_token": false,
-  "added_tokens_decoder": {
-    "0": {
-      "content": "<unk>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "1": {
-      "content": "<s>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "2": {
-      "content": "</s>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "92352": {
-      "content": "Ｅ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92353": {
-      "content": "Ｆ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92354": {
-      "content": "Ｇ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92355": {
-      "content": "Ｈ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92356": {
-      "content": "Ｉ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92357": {
-      "content": "Ｊ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92358": {
-      "content": "Ｋ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92359": {
-      "content": "Ｌ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92360": {
-      "content": "Ｍ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92361": {
-      "content": "Ｎ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92362": {
-      "content": "Ｒ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92363": {
-      "content": "Ｕ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92364": {
-      "content": "Ｖ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92365": {
-      "content": "Ｗ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92366": {
-      "content": "Ｘ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92367": {
-      "content": "Ｙ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92368": {
-      "content": "Ｚ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92369": {
-      "content": "ａ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92370": {
-      "content": "ｂ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92371": {
-      "content": "ｃ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92372": {
-      "content": "ｄ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92373": {
-      "content": "ｅ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92374": {
-      "content": "ｆ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92375": {
-      "content": "ｇ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92376": {
-      "content": "ｈ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92377": {
-      "content": "ｉ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92378": {
-      "content": "ｊ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92379": {
-      "content": "ｋ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92380": {
-      "content": "ｌ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92381": {
-      "content": "ｍ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92382": {
-      "content": "ｎ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92383": {
-      "content": "ｏ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92384": {
-      "content": "ｐ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92385": {
-      "content": "ｑ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92386": {
-      "content": "ｒ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92387": {
-      "content": "ｓ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92388": {
-      "content": "ｔ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92389": {
-      "content": "ｕ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92390": {
-      "content": "ｖ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92391": {
-      "content": "ｗ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92392": {
-      "content": "ｘ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92393": {
-      "content": "ｙ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92394": {
-      "content": "ｚ",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92395": {
-      "content": "——",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "92396": {
-      "content": "……",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
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-    },
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llama-factory/saves/glm-4-9b/lora/sft_bf16_p1_full/README.md

@@ -0,0 +1,70 @@
+---
+license: other
+library_name: peft
+tags:
+- llama-factory
+- lora
+- generated_from_trainer
+base_model: THUDM/glm-4-9b-chat-1m
+metrics:
+- accuracy
+model-index:
+- name: sft_bf16_p1_full
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# sft_bf16_p1_full
+
+This model is a fine-tuned version of [THUDM/glm-4-9b-chat-1m](https://huggingface.co/THUDM/glm-4-9b-chat-1m) on the alpaca_mgtv_p1 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1995
+- Accuracy: 0.9332
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0001
+- train_batch_size: 16
+- eval_batch_size: 1
+- seed: 42
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 128
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 4.0
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:--------:|
+| 0.8958        | 0.9950 | 175  | 0.4473          | 0.7613   |
+| 0.1917        | 1.9900 | 350  | 0.1856          | 0.9307   |
+| 0.1287        | 2.9851 | 525  | 0.1813          | 0.9337   |
+| 0.0755        | 3.9801 | 700  | 0.1995          | 0.9332   |
+
+
+### Framework versions
+
+- PEFT 0.11.1
+- Transformers 4.41.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1

llama-factory/saves/glm-4-9b/lora/sft_bf16_p1_full/adapter_config.json

@@ -0,0 +1,31 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "THUDM/glm-4-9b-chat-1m",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_dropout": 0.0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "dense_h_to_4h",
+    "query_key_value",
+    "dense_4h_to_h",
+    "dense"
+  ],
+  "task_type": "CAUSAL_LM",
+  "use_dora": false,
+  "use_rslora": false
+}

llama-factory/{merged_models/internlm2_5-7b-chat-1m_sft_bf16_p2_full/model-00002-of-00008.safetensors → saves/glm-4-9b/lora/sft_bf16_p1_full/adapter_model.safetensors}

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-oid sha256:a8ebc89c15e39300cdb8876edace963f8a1b03c9092bae105970c1b1b7c7c92a
-size 1946242696
+oid sha256:779d919e4e576eb536f72ff440fea92eb01a8b6522a276d586a48fc2f24d1fd2
+size 85409560

llama-factory/saves/glm-4-9b/lora/sft_bf16_p1_full/added_tokens.json

@@ -0,0 +1,16 @@
+{
+  "<eop>": 151334,
+  "<sop>": 151333,
+  "<|assistant|>": 151337,
+  "<|begin_of_image|>": 151339,
+  "<|begin_of_video|>": 151341,
+  "<|end_of_image|>": 151340,
+  "<|end_of_video|>": 151342,
+  "<|endoftext|>": 151329,
+  "<|observation|>": 151338,
+  "<|system|>": 151335,
+  "<|user|>": 151336,
+  "[MASK]": 151330,
+  "[gMASK]": 151331,
+  "[sMASK]": 151332
+}

llama-factory/saves/glm-4-9b/lora/sft_bf16_p1_full/all_results.json

@@ -0,0 +1,13 @@
+{
+    "epoch": 3.9800995024875623,
+    "eval_accuracy": 0.9332066666666665,
+    "eval_loss": 0.1994711458683014,
+    "eval_runtime": 135.9458,
+    "eval_samples_per_second": 18.39,
+    "eval_steps_per_second": 18.39,
+    "total_flos": 1.742929467193688e+18,
+    "train_loss": 0.2920300728934152,
+    "train_runtime": 10146.3014,
+    "train_samples_per_second": 8.87,
+    "train_steps_per_second": 0.069
+}

llama-factory/saves/glm-4-9b/lora/sft_bf16_p1_full/checkpoint-350/README.md

@@ -0,0 +1,202 @@
+---
+library_name: peft
+base_model: THUDM/glm-4-9b-chat-1m
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.11.1

llama-factory/saves/glm-4-9b/lora/sft_bf16_p1_full/checkpoint-350/adapter_config.json

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+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "THUDM/glm-4-9b-chat-1m",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_dropout": 0.0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "dense_h_to_4h",
+    "query_key_value",
+    "dense_4h_to_h",
+    "dense"
+  ],
+  "task_type": "CAUSAL_LM",
+  "use_dora": false,
+  "use_rslora": false
+}

llama-factory/{merged_models/internlm2_5-7b-chat-1m_sft_bf16_p2_full/model-00003-of-00008.safetensors → saves/glm-4-9b/lora/sft_bf16_p1_full/checkpoint-350/adapter_model.safetensors}

@@ -1,3 +1,3 @@
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llama-factory/saves/glm-4-9b/lora/sft_bf16_p1_full/checkpoint-350/added_tokens.json

@@ -0,0 +1,16 @@
+{
+  "<eop>": 151334,
+  "<sop>": 151333,
+  "<|assistant|>": 151337,
+  "<|begin_of_image|>": 151339,
+  "<|begin_of_video|>": 151341,
+  "<|end_of_image|>": 151340,
+  "<|end_of_video|>": 151342,
+  "<|endoftext|>": 151329,
+  "<|observation|>": 151338,
+  "<|system|>": 151335,
+  "<|user|>": 151336,
+  "[MASK]": 151330,
+  "[gMASK]": 151331,
+  "[sMASK]": 151332
+}

llama-factory/{merged_models/internlm2_5-7b-chat-1m_sft_bf16_p2_full/model-00004-of-00008.safetensors → saves/glm-4-9b/lora/sft_bf16_p1_full/checkpoint-350/optimizer.pt}

@@ -1,3 +1,3 @@
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+size 170990330

llama-factory/saves/glm-4-9b/lora/sft_bf16_p1_full/checkpoint-350/rng_state.pth

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+size 14244

llama-factory/saves/glm-4-9b/lora/sft_bf16_p1_full/checkpoint-350/scheduler.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:260dbe7a290a3eb6243acf2b7854e91fda974ddc05a7f2e6ef4d24d9dbbc4233
+size 1064

llama-factory/{merged_models/internlm2_5-7b-chat-1m_sft_bf16_p2_full → saves/glm-4-9b/lora/sft_bf16_p1_full/checkpoint-350}/special_tokens_map.json

@@ -1,35 +1,29 @@
 {
   "additional_special_tokens": [
-    "<|im_start|>",
-    "<|im_end|>",
-    "<|action_start|>",
-    "<|action_end|>",
-    "<|interpreter|>",
-    "<|plugin|>"
+    "<|endoftext|>",
+    "[MASK]",
+    "[gMASK]",
+    "[sMASK]",
+    "<sop>",
+    "<eop>",
+    "<|system|>",
+    "<|user|>",
+    "<|assistant|>",
+    "<|observation|>",
+    "<|begin_of_image|>",
+    "<|end_of_image|>",
+    "<|begin_of_video|>",
+    "<|end_of_video|>"
   ],
-  "bos_token": {
-    "content": "<s>",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
   "eos_token": {
-    "content": "</s>",
+    "content": "<|endoftext|>",
     "lstrip": false,
     "normalized": false,
     "rstrip": false,
     "single_word": false
   },
   "pad_token": {
-    "content": "</s>",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "unk_token": {
-    "content": "<unk>",
+    "content": "<|endoftext|>",
     "lstrip": false,
     "normalized": false,
     "rstrip": false,

llama-factory/saves/glm-4-9b/lora/sft_bf16_p1_full/checkpoint-350/tokenization_chatglm.py

@@ -0,0 +1,323 @@
+import regex as re
+import base64
+import os
+import json
+import tiktoken
+from torch import TensorType
+from typing import List, Optional, Union, Dict, Any
+from transformers import PreTrainedTokenizer
+from transformers.utils import logging, PaddingStrategy
+from transformers.tokenization_utils_base import EncodedInput, BatchEncoding
+
+
+class ChatGLM4Tokenizer(PreTrainedTokenizer):
+    vocab_files_names = {"vocab_file": "tokenizer.model"}
+    model_input_names = ["input_ids", "attention_mask", "position_ids"]
+
+    def __init__(
+            self,
+            vocab_file,
+            padding_side="left",
+            clean_up_tokenization_spaces=False,
+            encode_special_tokens=False,
+            **kwargs
+    ):
+        self.name = "GLM4Tokenizer"
+        self.vocab_file = vocab_file
+        pat_str = "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"
+        self.pat_str = re.compile(pat_str)
+        self.encode_special_tokens = encode_special_tokens
+
+        mergeable_ranks = {}
+        with open(vocab_file) as f:
+            for line in f:
+                token, rank = line.strip().split()
+                rank = int(rank)
+                token = base64.b64decode(token)
+                mergeable_ranks[token] = rank
+
+        self.mergeable_ranks = mergeable_ranks
+
+        self.tokenizer = tiktoken.Encoding(
+            name="my_tokenizer",
+            pat_str=pat_str,
+            mergeable_ranks=mergeable_ranks,
+            special_tokens={}
+        )
+        self.decoder = {rank: token for token, rank in mergeable_ranks.items()}
+        self.n_words = len(self.decoder)
+
+        super().__init__(
+            padding_side=padding_side,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            **kwargs
+        )
+
+    @property
+    def vocab_size(self):
+        return self.n_words
+
+    def get_vocab(self):
+        """ Returns vocab as a dict """
+        vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)}
+        vocab.update(self.added_tokens_encoder)
+        return vocab
+
+    def convert_tokens_to_string(self, tokens: List[Union[bytes, str, int]]) -> str:
+        """
+        Converts a sequence of tokens in a single string.
+        """
+        text = ""
+        temp = b""
+        for t in tokens:
+            if isinstance(t, int):
+                t = chr(t)
+            if isinstance(t, str):
+                if temp:
+                    text += temp.decode("utf-8", errors="replace")
+            elif isinstance(t, bytes):
+                temp += t
+            else:
+                raise TypeError("token should only be of type int, bytes or str")
+        if temp:
+            text += temp.decode("utf-8", errors="replace")
+        return text
+
+    def _tokenize(self, text, **kwargs):
+        tokens = []
+        ids = self.tokenizer.encode(text)
+        for t in ids:
+            tokens.append(self.decoder[t])
+        return tokens
+
+    def _convert_token_to_id(self, token):
+        """ Converts a token (str) in an id using the vocab. """
+        return self.mergeable_ranks[token]
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        return self.decoder.get(index, "")
+
+    def save_vocabulary(self, save_directory, filename_prefix=None):
+        """
+        Save the vocabulary and special tokens file to a directory.
+
+        Args:
+            save_directory (`str`):
+                The directory in which to save the vocabulary.
+            filename_prefix (`str`, *optional*):
+                An optional prefix to add to the named of the saved files.
+
+        Returns:
+            `Tuple(str)`: Paths to the files saved.
+        """
+        if os.path.isdir(save_directory):
+            vocab_file = os.path.join(
+                save_directory, self.vocab_files_names["vocab_file"]
+            )
+        else:
+            vocab_file = save_directory
+
+        with open(self.vocab_file, 'rb') as fin:
+            proto_str = fin.read()
+
+        with open(vocab_file, "wb") as writer:
+            writer.write(proto_str)
+
+        return (vocab_file,)
+
+    def get_prefix_tokens(self):
+        prefix_tokens = [self.convert_tokens_to_ids("[gMASK]"), self.convert_tokens_to_ids("<sop>")]
+        return prefix_tokens
+
+    def build_single_message(self, role, metadata, message, tokenize=True):
+        assert role in ["system", "user", "assistant", "observation"], role
+        if tokenize:
+            role_tokens = [self.convert_tokens_to_ids(f"<|{role}|>")] + self.tokenizer.encode(f"{metadata}\n",
+                                                                                              disallowed_special=())
+            message_tokens = self.tokenizer.encode(message, disallowed_special=())
+            tokens = role_tokens + message_tokens
+            return tokens
+        else:
+            return str(f"<|{role}|>{metadata}\n{message}")
+
+    # Use Jinja Template in tokenizer_config.json
+    # def apply_chat_template(
+    #         self,
+    #         conversation: Union[List[Dict[str, str]], List[List[Dict[str, str]]], "Conversation"],
+    #         add_generation_prompt: bool = False,
+    #         tokenize: bool = True,
+    #         padding: bool = False,
+    #         truncation: bool = False,
+    #         max_length: Optional[int] = None,
+    #         return_tensors: Optional[Union[str, TensorType]] = None,
+    #         return_dict: bool = False,
+    #         tokenizer_kwargs: Optional[Dict[str, Any]] = None,
+    #         add_special_tokens: bool = True,
+    #         **kwargs,
+    # ) -> Union[str, List[int], List[str], List[List[int]], BatchEncoding]:
+    #
+    #     if return_dict and not tokenize:
+    #         raise ValueError(
+    #             "`return_dict=True` is incompatible with `tokenize=False`, because there is no dict "
+    #             "of tokenizer outputs to return."
+    #         )
+    #
+    #     def handle_single_conversation(conversation):
+    #         input_ids = self.get_prefix_tokens() if add_special_tokens else []
+    #         input_message = "[gMASK]<sop>" if add_special_tokens else ""
+    #         for item in conversation:
+    #             if item.get("tools"):
+    #                 tools = item["tools"]
+    #                 content = "你是一个名为 GhatGLM 的人工智能助手。你是基于智谱AI训练的语言模型 GLM-4 模型开发的，你的任务是针对用户的问题和要求提供适当的答复和支持。"
+    #                 content += "\n\n# 可用工具"
+    #                 for tool in tools:
+    #                     if tool["type"] == "function":
+    #                         function = tool["function"]
+    #                         content += f"\n\n## {function['name']}\n\n{json.dumps(function, ensure_ascii=False, indent=4)}"
+    #                         content += "\n在调用上述函数时，请使用 Json 格式表示调用的参数。"
+    #                     elif tool["type"] == "python":
+    #                         content += "\n\n## python\n\n当你向 `python` 发送包含 Python 代码的消息时，该代码将会在一个有状态的 Jupyter notebook 环境中执行。\n`python` 返回代码执行的输出，或在执行 60 秒后返回超时。\n`/mnt/data` 将会持久化存储你的文件。在此会话中，`python` 无法访问互联网。不要使用 `python` 进行任何网络请求或者在线 API 调用，这些在线内容的访问将不会成功。"
+    #                     elif tool["type"] == "simple_browser":
+    #                         content += "\n\n## simple_browser\n\n你可以使用 `simple_browser` 工具。该工具支持以下函数：\n`search(query: str, recency_days: int)`：使用搜索引擎进行查询并显示结果，可以使用 `recency_days` 参数控制搜索内容的时效性。\n`mclick(ids: list[int])`：获取一系列指定 id 的页面内容。每次调用时，须选择3-10个页面。选择多个角度的页面，同时尽可能选择可信任的信息来源。考虑到部分页面是无法加载的，你也可以多打开一些可能有用的页面而不用担心内容过多。\n`open_url(url: str)`：打开指定的 URL。\n\n使用 `【{引用 id}†{引用文本}】` 来引用内容。\n\n操作步骤：1. 使用 `search` 来获得信息列表; 2. 使用 `mclick` 来获取指定 ID 页面的内容; 3. 根据获得的内容进行回复。在回复中应当引用信息来源。\n 如果用户提供了 URL，也可以用 `open_url` 直接打开页面。\n如果初次搜索结果没有找到合适的信息，也可以再次使用 `search` 进行搜索。"
+    #                     elif tool["type"] == "cogview":
+    #                         content += "\n\n## cogview\n\n如果用户的请求中包含了对图像的描述，你可以使用 `cogview` 来生成图像并展示给用户。你需要向 `cogview` 发送图像描述，规则：\n- 发送给 `cogview` 的消息必须使用英语。用户的中文描述必须完全翻译为英语。\n- 应当尽可能详细地描述图像生成的需求，需求描述约 100 英文单词。\n- 保持用户原始描述的意图。不要虚构内容或者没见过的人物。\n- 如无特殊说明，所在地为中国，持有中国立场并遵循中国社会主义价值观。"
+    #                     else:
+    #                         raise NotImplementedError(f"Unknown tool type {tool['type']}")
+    #                 input = self.build_single_message("system", "", content, tokenize=tokenize)
+    #                 if tokenize:
+    #                     input_ids.extend(input)
+    #                 else:
+    #                     input_message += input
+    #             if item["content"]:
+    #                 input = self.build_single_message(
+    #                     item["role"],
+    #                     item.get("metadata", ""),
+    #                     item["content"],
+    #                     tokenize=tokenize
+    #                 )
+    #                 if tokenize:
+    #                     input_ids.extend(input)
+    #                 else:
+    #                     input_message += input
+    #         if add_generation_prompt:
+    #             if tokenize:
+    #                 input_ids.extend([self.convert_tokens_to_ids("<|assistant|>")])
+    #             else:
+    #                 input_message += "<|assistant|>"
+    #         return input_ids if tokenize else input_message
+    #
+    #     # Main logic to handle different conversation formats
+    #     if isinstance(conversation, list) and all(isinstance(i, dict) for i in conversation):
+    #         result = handle_single_conversation(conversation)
+    #     elif isinstance(conversation, list) and all(isinstance(i, list) for i in conversation):
+    #         result = [handle_single_conversation(c) for c in conversation]
+    #     elif hasattr(conversation, "messages"):
+    #         result = handle_single_conversation(conversation.messages)
+    #     else:
+    #         raise ValueError("Invalid conversation format")
+    #
+    #     if tokenize:
+    #         output = self.batch_encode_plus(
+    #             [result] if isinstance(result[0], int) else result,
+    #             padding=padding,
+    #             truncation=truncation,
+    #             max_length=max_length,
+    #             return_tensors=return_tensors,
+    #             is_split_into_words=True,
+    #             add_special_tokens=False
+    #         )
+    #         if return_dict:
+    #             return output
+    #         else:
+    #             return output["input_ids"]
+    #     else:
+    #         return result
+
+    def build_inputs_with_special_tokens(
+            self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+        adding special tokens. A BERT sequence has the following format:
+
+        - single sequence: `[CLS] X [SEP]`
+        - pair of sequences: `[CLS] A [SEP] B [SEP]`
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs to which the special tokens will be added.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+        """
+        prefix_tokens = self.get_prefix_tokens()
+        token_ids_0 = prefix_tokens + token_ids_0
+        if token_ids_1 is not None:
+            token_ids_0 = token_ids_0 + token_ids_1 + [self.convert_tokens_to_ids("<eos>")]
+        return token_ids_0
+
+    def _pad(
+            self,
+            encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
+            max_length: Optional[int] = None,
+            padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+            pad_to_multiple_of: Optional[int] = None,
+            return_attention_mask: Optional[bool] = None,
+    ) -> dict:
+        """
+        Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
+
+        Args:
+            encoded_inputs:
+                Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
+            max_length: maximum length of the returned list and optionally padding length (see below).
+                Will truncate by taking into account the special tokens.
+            padding_strategy: PaddingStrategy to use for padding.
+
+                - PaddingStrategy.LONGEST Pad to the longest sequence in the batch
+                - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
+                - PaddingStrategy.DO_NOT_PAD: Do not pad
+                The tokenizer padding sides are defined in self.padding_side:
+
+                    - 'left': pads on the left of the sequences
+                    - 'right': pads on the right of the sequences
+            pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
+                This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
+                `>= 7.5` (Volta).
+            return_attention_mask:
+                (optional) Set to False to avoid returning attention mask (default: set to model specifics)
+        """
+        # Load from model defaults
+        assert self.padding_side == "left"
+
+        required_input = encoded_inputs[self.model_input_names[0]]
+        seq_length = len(required_input)
+
+        if padding_strategy == PaddingStrategy.LONGEST:
+            max_length = len(required_input)
+
+        if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
+            max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
+
+        needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
+
+        # Initialize attention mask if not present.
+        if "attention_mask" not in encoded_inputs:
+            encoded_inputs["attention_mask"] = [1] * seq_length
+
+        if "position_ids" not in encoded_inputs:
+            encoded_inputs["position_ids"] = list(range(seq_length))
+
+        if needs_to_be_padded:
+            difference = max_length - len(required_input)
+
+            if "attention_mask" in encoded_inputs:
+                encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
+            if "position_ids" in encoded_inputs:
+                encoded_inputs["position_ids"] = [0] * difference + encoded_inputs["position_ids"]
+            encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
+
+        return encoded_inputs

llama-factory/{merged_models/internlm2_5-7b-chat-1m_sft_bf16_p2_full → saves/glm-4-9b/lora/sft_bf16_p1_full/checkpoint-350}/tokenizer.model

@@ -1,3 +1,3 @@
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-oid sha256:f868398fc4e05ee1e8aeba95ddf18ddcc45b8bce55d5093bead5bbf80429b48b
-size 1477754
+oid sha256:5a493598071550244b2ee7f26118f3edec2150b9dfa967929a99052ac83fe716
+size 2623634

llama-factory/saves/glm-4-9b/lora/sft_bf16_p1_full/checkpoint-350/tokenizer_config.json

@@ -0,0 +1,148 @@
+{
+  "added_tokens_decoder": {
+    "151329": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151330": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151331": {
+      "content": "[gMASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151332": {
+      "content": "[sMASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151333": {
+      "content": "<sop>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151334": {
+      "content": "<eop>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151335": {
+      "content": "<|system|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+---
+library_name: peft
+base_model: THUDM/glm-4-9b-chat-1m
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
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+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.11.1

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+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

llama-factory/saves/glm-4-9b/lora/sft_bf16_p1_full/checkpoint-525/tokenization_chatglm.py

@@ -0,0 +1,323 @@
+import regex as re
+import base64
+import os
+import json
+import tiktoken
+from torch import TensorType
+from typing import List, Optional, Union, Dict, Any
+from transformers import PreTrainedTokenizer
+from transformers.utils import logging, PaddingStrategy
+from transformers.tokenization_utils_base import EncodedInput, BatchEncoding
+
+
+class ChatGLM4Tokenizer(PreTrainedTokenizer):
+    vocab_files_names = {"vocab_file": "tokenizer.model"}
+    model_input_names = ["input_ids", "attention_mask", "position_ids"]
+
+    def __init__(
+            self,
+            vocab_file,
+            padding_side="left",
+            clean_up_tokenization_spaces=False,
+            encode_special_tokens=False,
+            **kwargs
+    ):
+        self.name = "GLM4Tokenizer"
+        self.vocab_file = vocab_file
+        pat_str = "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"
+        self.pat_str = re.compile(pat_str)
+        self.encode_special_tokens = encode_special_tokens
+
+        mergeable_ranks = {}
+        with open(vocab_file) as f:
+            for line in f:
+                token, rank = line.strip().split()
+                rank = int(rank)
+                token = base64.b64decode(token)
+                mergeable_ranks[token] = rank
+
+        self.mergeable_ranks = mergeable_ranks
+
+        self.tokenizer = tiktoken.Encoding(
+            name="my_tokenizer",
+            pat_str=pat_str,
+            mergeable_ranks=mergeable_ranks,
+            special_tokens={}
+        )
+        self.decoder = {rank: token for token, rank in mergeable_ranks.items()}
+        self.n_words = len(self.decoder)
+
+        super().__init__(
+            padding_side=padding_side,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            **kwargs
+        )
+
+    @property
+    def vocab_size(self):
+        return self.n_words
+
+    def get_vocab(self):
+        """ Returns vocab as a dict """
+        vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)}
+        vocab.update(self.added_tokens_encoder)
+        return vocab
+
+    def convert_tokens_to_string(self, tokens: List[Union[bytes, str, int]]) -> str:
+        """
+        Converts a sequence of tokens in a single string.
+        """
+        text = ""
+        temp = b""
+        for t in tokens:
+            if isinstance(t, int):
+                t = chr(t)
+            if isinstance(t, str):
+                if temp:
+                    text += temp.decode("utf-8", errors="replace")
+            elif isinstance(t, bytes):
+                temp += t
+            else:
+                raise TypeError("token should only be of type int, bytes or str")
+        if temp:
+            text += temp.decode("utf-8", errors="replace")
+        return text
+
+    def _tokenize(self, text, **kwargs):
+        tokens = []
+        ids = self.tokenizer.encode(text)
+        for t in ids:
+            tokens.append(self.decoder[t])
+        return tokens
+
+    def _convert_token_to_id(self, token):
+        """ Converts a token (str) in an id using the vocab. """
+        return self.mergeable_ranks[token]
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        return self.decoder.get(index, "")
+
+    def save_vocabulary(self, save_directory, filename_prefix=None):
+        """
+        Save the vocabulary and special tokens file to a directory.
+
+        Args:
+            save_directory (`str`):
+                The directory in which to save the vocabulary.
+            filename_prefix (`str`, *optional*):
+                An optional prefix to add to the named of the saved files.
+
+        Returns:
+            `Tuple(str)`: Paths to the files saved.
+        """
+        if os.path.isdir(save_directory):
+            vocab_file = os.path.join(
+                save_directory, self.vocab_files_names["vocab_file"]
+            )
+        else:
+            vocab_file = save_directory
+
+        with open(self.vocab_file, 'rb') as fin:
+            proto_str = fin.read()
+
+        with open(vocab_file, "wb") as writer:
+            writer.write(proto_str)
+
+        return (vocab_file,)
+
+    def get_prefix_tokens(self):
+        prefix_tokens = [self.convert_tokens_to_ids("[gMASK]"), self.convert_tokens_to_ids("<sop>")]
+        return prefix_tokens
+
+    def build_single_message(self, role, metadata, message, tokenize=True):
+        assert role in ["system", "user", "assistant", "observation"], role
+        if tokenize:
+            role_tokens = [self.convert_tokens_to_ids(f"<|{role}|>")] + self.tokenizer.encode(f"{metadata}\n",
+                                                                                              disallowed_special=())
+            message_tokens = self.tokenizer.encode(message, disallowed_special=())
+            tokens = role_tokens + message_tokens
+            return tokens
+        else:
+            return str(f"<|{role}|>{metadata}\n{message}")
+
+    # Use Jinja Template in tokenizer_config.json
+    # def apply_chat_template(
+    #         self,
+    #         conversation: Union[List[Dict[str, str]], List[List[Dict[str, str]]], "Conversation"],
+    #         add_generation_prompt: bool = False,
+    #         tokenize: bool = True,
+    #         padding: bool = False,
+    #         truncation: bool = False,
+    #         max_length: Optional[int] = None,
+    #         return_tensors: Optional[Union[str, TensorType]] = None,
+    #         return_dict: bool = False,
+    #         tokenizer_kwargs: Optional[Dict[str, Any]] = None,
+    #         add_special_tokens: bool = True,
+    #         **kwargs,
+    # ) -> Union[str, List[int], List[str], List[List[int]], BatchEncoding]:
+    #
+    #     if return_dict and not tokenize:
+    #         raise ValueError(
+    #             "`return_dict=True` is incompatible with `tokenize=False`, because there is no dict "
+    #             "of tokenizer outputs to return."
+    #         )
+    #
+    #     def handle_single_conversation(conversation):
+    #         input_ids = self.get_prefix_tokens() if add_special_tokens else []
+    #         input_message = "[gMASK]<sop>" if add_special_tokens else ""
+    #         for item in conversation:
+    #             if item.get("tools"):
+    #                 tools = item["tools"]
+    #                 content = "你是一个名为 GhatGLM 的人工智能助手。你是基于智谱AI训练的语言模型 GLM-4 模型开发的，你的任务是针对用户的问题和要求提供适当的答复和支持。"
+    #                 content += "\n\n# 可用工具"
+    #                 for tool in tools:
+    #                     if tool["type"] == "function":
+    #                         function = tool["function"]
+    #                         content += f"\n\n## {function['name']}\n\n{json.dumps(function, ensure_ascii=False, indent=4)}"
+    #                         content += "\n在调用上述函数时，请使用 Json 格式表示调用的参数。"
+    #                     elif tool["type"] == "python":
+    #                         content += "\n\n## python\n\n当你向 `python` 发送包含 Python 代码的消息时，该代码将会在一个有状态的 Jupyter notebook 环境中执行。\n`python` 返回代码执行的输出，或在执行 60 秒后返回超时。\n`/mnt/data` 将会持久化存储你的文件。在此会话中，`python` 无法访问互联网。不要使用 `python` 进行任何网络请求或者在线 API 调用，这些在线内容的访问将不会成功。"
+    #                     elif tool["type"] == "simple_browser":
+    #                         content += "\n\n## simple_browser\n\n你可以使用 `simple_browser` 工具。该工具支持以下函数：\n`search(query: str, recency_days: int)`：使用搜索引擎进行查询并显示结果，可以使用 `recency_days` 参数控制搜索内容的时效性。\n`mclick(ids: list[int])`：获取一系列指定 id 的页面内容。每次调用时，须选择3-10个页面。选择多个角度的页面，同时尽可能选择可信任的信息来源。考虑到部分页面是无法加载的，你也可以多打开一些可能有用的页面而不用担心内容过多。\n`open_url(url: str)`：打开指定的 URL。\n\n使用 `【{引用 id}†{引用文本}】` 来引用内容。\n\n操作步骤：1. 使用 `search` 来获得信息列表; 2. 使用 `mclick` 来获取指定 ID 页面的内容; 3. 根据获得的内容进行回复。在回复中应当引用信息来源。\n 如果用户提供了 URL，也可以用 `open_url` 直接打开页面。\n如果初次搜索结果没有找到合适的信息，也可以再次使用 `search` 进行搜索。"
+    #                     elif tool["type"] == "cogview":
+    #                         content += "\n\n## cogview\n\n如果用户的请求中包含了对图像的描述，你可以使用 `cogview` 来生成图像并展示给用户。你需要向 `cogview` 发送图像描述，规则：\n- 发送给 `cogview` 的消息必须使用英语。用户的中文描述必须完全翻译为英语。\n- 应当尽可能详细地描述图像生成的需求，需求描述约 100 英文单词。\n- 保持用户原始描述的意图。不要虚构内容或者没见过的人物。\n- 如无特殊说明，所在地为中国，持有中国立场并遵循中国社会主义价值观。"
+    #                     else:
+    #                         raise NotImplementedError(f"Unknown tool type {tool['type']}")
+    #                 input = self.build_single_message("system", "", content, tokenize=tokenize)
+    #                 if tokenize:
+    #                     input_ids.extend(input)
+    #                 else:
+    #                     input_message += input
+    #             if item["content"]:
+    #                 input = self.build_single_message(
+    #                     item["role"],
+    #                     item.get("metadata", ""),
+    #                     item["content"],
+    #                     tokenize=tokenize
+    #                 )
+    #                 if tokenize:
+    #                     input_ids.extend(input)
+    #                 else:
+    #                     input_message += input
+    #         if add_generation_prompt:
+    #             if tokenize:
+    #                 input_ids.extend([self.convert_tokens_to_ids("<|assistant|>")])
+    #             else:
+    #                 input_message += "<|assistant|>"
+    #         return input_ids if tokenize else input_message
+    #
+    #     # Main logic to handle different conversation formats
+    #     if isinstance(conversation, list) and all(isinstance(i, dict) for i in conversation):
+    #         result = handle_single_conversation(conversation)
+    #     elif isinstance(conversation, list) and all(isinstance(i, list) for i in conversation):
+    #         result = [handle_single_conversation(c) for c in conversation]
+    #     elif hasattr(conversation, "messages"):
+    #         result = handle_single_conversation(conversation.messages)
+    #     else:
+    #         raise ValueError("Invalid conversation format")
+    #
+    #     if tokenize:
+    #         output = self.batch_encode_plus(
+    #             [result] if isinstance(result[0], int) else result,
+    #             padding=padding,
+    #             truncation=truncation,
+    #             max_length=max_length,
+    #             return_tensors=return_tensors,
+    #             is_split_into_words=True,
+    #             add_special_tokens=False
+    #         )
+    #         if return_dict:
+    #             return output
+    #         else:
+    #             return output["input_ids"]
+    #     else:
+    #         return result
+
+    def build_inputs_with_special_tokens(
+            self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+        adding special tokens. A BERT sequence has the following format:
+
+        - single sequence: `[CLS] X [SEP]`
+        - pair of sequences: `[CLS] A [SEP] B [SEP]`
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs to which the special tokens will be added.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+        """
+        prefix_tokens = self.get_prefix_tokens()
+        token_ids_0 = prefix_tokens + token_ids_0
+        if token_ids_1 is not None:
+            token_ids_0 = token_ids_0 + token_ids_1 + [self.convert_tokens_to_ids("<eos>")]
+        return token_ids_0
+
+    def _pad(
+            self,
+            encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
+            max_length: Optional[int] = None,
+            padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+            pad_to_multiple_of: Optional[int] = None,
+            return_attention_mask: Optional[bool] = None,
+    ) -> dict:
+        """
+        Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
+
+        Args:
+            encoded_inputs:
+                Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
+            max_length: maximum length of the returned list and optionally padding length (see below).
+                Will truncate by taking into account the special tokens.
+            padding_strategy: PaddingStrategy to use for padding.
+
+                - PaddingStrategy.LONGEST Pad to the longest sequence in the batch
+                - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
+                - PaddingStrategy.DO_NOT_PAD: Do not pad
+                The tokenizer padding sides are defined in self.padding_side:
+
+                    - 'left': pads on the left of the sequences
+                    - 'right': pads on the right of the sequences
+            pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
+                This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
+                `>= 7.5` (Volta).
+            return_attention_mask:
+                (optional) Set to False to avoid returning attention mask (default: set to model specifics)
+        """
+        # Load from model defaults
+        assert self.padding_side == "left"
+
+        required_input = encoded_inputs[self.model_input_names[0]]
+        seq_length = len(required_input)
+
+        if padding_strategy == PaddingStrategy.LONGEST:
+            max_length = len(required_input)
+
+        if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
+            max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
+
+        needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
+
+        # Initialize attention mask if not present.
+        if "attention_mask" not in encoded_inputs:
+            encoded_inputs["attention_mask"] = [1] * seq_length
+
+        if "position_ids" not in encoded_inputs:
+            encoded_inputs["position_ids"] = list(range(seq_length))
+
+        if needs_to_be_padded:
+            difference = max_length - len(required_input)
+
+            if "attention_mask" in encoded_inputs:
+                encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
+            if "position_ids" in encoded_inputs:
+                encoded_inputs["position_ids"] = [0] * difference + encoded_inputs["position_ids"]
+            encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
+
+        return encoded_inputs

llama-factory/saves/glm-4-9b/lora/sft_bf16_p1_full/checkpoint-525/tokenizer.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5a493598071550244b2ee7f26118f3edec2150b9dfa967929a99052ac83fe716
+size 2623634

llama-factory/saves/glm-4-9b/lora/sft_bf16_p1_full/checkpoint-525/tokenizer_config.json

@@ -0,0 +1,148 @@
+{
+  "added_tokens_decoder": {
+    "151329": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151330": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151331": {
+      "content": "[gMASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151332": {
+      "content": "[sMASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151333": {
+      "content": "<sop>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151334": {
+      "content": "<eop>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151335": {
+      "content": "<|system|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151336": {
+      "content": "<|user|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151337": {
+      "content": "<|assistant|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151338": {
+      "content": "<|observation|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151339": {
+      "content": "<|begin_of_image|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151340": {
+      "content": "<|end_of_image|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151341": {
+      "content": "<|begin_of_video|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151342": {
+      "content": "<|end_of_video|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<|endoftext|>",
+    "[MASK]",
+    "[gMASK]",
+    "[sMASK]",
+    "<sop>",
+    "<eop>",
+    "<|system|>",
+    "<|user|>",
+    "<|assistant|>",
+    "<|observation|>",
+    "<|begin_of_image|>",
+    "<|end_of_image|>",
+    "<|begin_of_video|>",
+    "<|end_of_video|>"
+  ],
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenization_chatglm.ChatGLM4Tokenizer",
+      null
+    ]
+  },
+  "chat_template": "{{ '[gMASK]<sop>' }}{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% endif %}{% if system_message is defined %}{{ '<|system|>\n' + system_message }}{% endif %}{% for message in messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|user|>\n' + content + '<|assistant|>' }}{% elif message['role'] == 'assistant' %}{{ '\n' + content }}{% endif %}{% endfor %}",
+  "clean_up_tokenization_spaces": false,
+  "do_lower_case": false,
+  "eos_token": "<|endoftext|>",
+  "model_max_length": 1024000,
+  "pad_token": "<|endoftext|>",
+  "padding_side": "right",
+  "remove_space": false,
+  "split_special_tokens": false,
+  "tokenizer_class": "ChatGLM4Tokenizer"
+}

llama-factory/saves/glm-4-9b/lora/sft_bf16_p1_full/checkpoint-525/trainer_state.json

@@ -0,0 +1,424 @@
+{
+  "best_metric": null,
+  "best_model_checkpoint": null,
+  "epoch": 2.9850746268656714,
+  "eval_steps": 175,
+  "global_step": 525,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "epoch": 0.05685856432125089,
+      "grad_norm": 2.919694662094116,
+      "learning_rate": 1.4285714285714285e-05,
+      "loss": 3.8009,
+      "step": 10
+    },
+    {
+      "epoch": 0.11371712864250177,
+      "grad_norm": 3.130059003829956,
+      "learning_rate": 2.857142857142857e-05,
+      "loss": 0.3289,
+      "step": 20
+    },
+    {
+      "epoch": 0.17057569296375266,
+      "grad_norm": 1.6621949672698975,
+      "learning_rate": 4.2857142857142856e-05,
+      "loss": 0.2598,
+      "step": 30
+    },
+    {
+      "epoch": 0.22743425728500355,
+      "grad_norm": 2.371370792388916,
+      "learning_rate": 5.714285714285714e-05,
+      "loss": 0.2401,
+      "step": 40
+    },
+    {
+      "epoch": 0.28429282160625446,
+      "grad_norm": 0.8625539541244507,
+      "learning_rate": 7.142857142857143e-05,
+      "loss": 0.2306,
+      "step": 50
+    },
+    {
+      "epoch": 0.3411513859275053,
+      "grad_norm": 5.281027793884277,
+      "learning_rate": 8.571428571428571e-05,
+      "loss": 0.2463,
+      "step": 60
+    },
+    {
+      "epoch": 0.39800995024875624,
+      "grad_norm": 1.4231479167938232,
+      "learning_rate": 0.0001,
+      "loss": 0.2291,
+      "step": 70
+    },
+    {
+      "epoch": 0.4548685145700071,
+      "grad_norm": 1.9341018199920654,
+      "learning_rate": 9.993784606094612e-05,
+      "loss": 0.2301,
+      "step": 80
+    },
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+---
+library_name: peft
+base_model: THUDM/glm-4-9b-chat-1m
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
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+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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+[More Information Needed]
+
+### Results
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+
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+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
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+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
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+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
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+
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+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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+## More Information [optional]
+
+[More Information Needed]
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+## Model Card Authors [optional]
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+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.11.1

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