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README.md

@@ -1,228 +1,207 @@
 ---
-license: apache-2.0
 library_name: peft
-tags:
-- mistral
-datasets:
-- jondurbin/airoboros-2.2.1
-inference: false
-pipeline_tag: text-generation
 base_model: mistralai/Mistral-7B-v0.1
 ---
 
-<div align="center">
+# Model Card for Model ID
 
-<img src="./logo.png" width="100px">
+<!-- Provide a quick summary of what the model is/does. -->
 
-</div>
 
-# Mistral-7B-Instruct-v0.1
-
-The Mistral-7B-Instruct-v0.1 LLM is a pretrained generative text model with 7 billion parameters geared towards instruction-following capabilities.
 
 ## Model Details
 
-This model was built via parameter-efficient finetuning of the [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) base model on the [jondurbin/airoboros-2.2.1](https://huggingface.co/datasets/jondurbin/airoboros-2.2.1) dataset. Finetuning was executed on 1x A100 (40 GB SXM) for roughly 3 hours.
-
-- **Developed by:** Daniel Furman
-- **Model type:** Decoder-only
-- **Language(s) (NLP):** English
-- **License:** Apache 2.0
-- **Finetuned from model:** [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
-
-## Model Sources 
-
-- **Repository:** [github.com/daniel-furman/sft-demos](https://github.com/daniel-furman/sft-demos/blob/main/src/sft/one_gpu/mistral/sft-mistral-7b-instruct-peft.ipynb)
-
-## Evaluation Results
-
-| Metric                | Value |
-|-----------------------|-------|
-| MMLU (5-shot)         | Coming |
-| ARC (25-shot)         | Coming |
-| HellaSwag (10-shot)   | Coming |
-| TruthfulQA (0-shot)   | Coming |
-| Avg.                  | Coming |
-
-We use Eleuther.AI's [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) to run the benchmark tests above, the same version as Hugging Face's [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
-
-## Basic Usage
-
-<details>
-
-<summary>Setup</summary>
-
-```python
-!pip install -q -U transformers peft torch accelerate bitsandbytes einops sentencepiece
-
-import torch
-from peft import PeftModel, PeftConfig
-from transformers import (
-    AutoModelForCausalLM,
-    AutoTokenizer,
-    BitsAndBytesConfig,
-)
-```
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [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 Data 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
 
-```python
-peft_model_id = "dfurman/Mistral-7B-Instruct-v0.1"
-config = PeftConfig.from_pretrained(peft_model_id)
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
 
-tokenizer = AutoTokenizer.from_pretrained(
-    peft_model_id,
-    use_fast=True,
-    trust_remote_code=True,
-)
-bnb_config = BitsAndBytesConfig(
-    load_in_4bit=True,
-    bnb_4bit_quant_type="nf4",
-    bnb_4bit_compute_dtype=torch.bfloat16,
-)
-model = AutoModelForCausalLM.from_pretrained(
-    config.base_model_name_or_path,
-    quantization_config=bnb_config,
-    device_map="auto",
-    trust_remote_code=True,
-)
-model = PeftModel.from_pretrained(
-    model, 
-    peft_model_id
-)
-```
+#### Speeds, Sizes, Times [optional]
 
-</details>
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
 
+[More Information Needed]
 
-```python
-messages = [
-    {"role": "user", "content": "Tell me a recipe for a mai tai."},
-]
+## Evaluation
 
-print("\n\n*** Prompt:")
-prompt = tokenizer.apply_chat_template(
-    messages,
-    tokenize=False,
-    add_generation_prompt=True
-)
-print(prompt)
+<!-- This section describes the evaluation protocols and provides the results. -->
 
-print("\n\n*** Generate:")
-input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda()
-with torch.autocast("cuda", dtype=torch.bfloat16):
-    output = model.generate(
-        input_ids=input_ids,
-        max_new_tokens=1024,
-        do_sample=True,
-        temperature=0.7,
-        return_dict_in_generate=True,
-        eos_token_id=tokenizer.eos_token_id,
-        pad_token_id=tokenizer.pad_token_id,
-        repetition_penalty=1.2,
-        no_repeat_ngram_size=5,
-    )
-
-response = tokenizer.decode(
-    output["sequences"][0][len(input_ids[0]):], 
-    skip_special_tokens=True
-)
-print(response)
-```
-
-<details>
-
-<summary>Output</summary>
-
-**Prompt**: 
-```python
-coming
-```
-
-**Generation**:
-```python
-coming
-```
-
-</details>
-
-
-## Speeds, Sizes, Times 
-
-| runtime / 50 tokens (sec) | GPU             | attn | torch dtype | VRAM (GB) |
-|:-----------------------------:|:----------------------:|:---------------------:|:-------------:|:-----------------------:|
-| 3.1                        | 1x A100 (40 GB SXM)  | torch               | fp16    | 13                    |
-
-## Training
-
-It took ~3 hours to train 3 epochs on 1x A100 (40 GB SXM).
-
-### Prompt Format
-
-This model was finetuned with the following format:
-
-```python
-tokenizer.chat_template = "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST] ' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token + ' ' }}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}"
-```
-
-
-This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method. Here's an illustrative example:
-
-```python
-messages = [
-    {"role": "user", "content": "What is your favourite condiment?"},
-    {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
-    {"role": "user", "content": "Do you have mayonnaise recipes?"}
-]
-prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-print(prompt)
-```
-
-<details>
-
-<summary>Output</summary>
+### Testing Data, Factors & Metrics
 
-```python
-coming
-```
-</details>
+#### Testing Data
 
-### Training Hyperparameters
+<!-- This should link to a Data Card if possible. -->
 
+[More Information Needed]
 
-We use the [SFTTrainer](https://huggingface.co/docs/trl/main/en/sft_trainer) from `trl` to fine-tune LLMs on instruction-following datasets.
+#### Factors
 
-The following `TrainingArguments` config was used:
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
 
-- num_train_epochs = 1
-- auto_find_batch_size = True
-- gradient_accumulation_steps = 1
-- optim = "paged_adamw_32bit"
-- save_strategy = "epoch"
-- learning_rate = 3e-4
-- lr_scheduler_type = "cosine"
-- warmup_ratio = 0.03
-- logging_strategy = "steps"
-- logging_steps = 25
-- bf16 = True
+[More Information Needed]
 
-The following `bitsandbytes` quantization config was used:
+#### Metrics
 
-- quant_method: bitsandbytes
-- load_in_8bit: False
-- load_in_4bit: True
-- llm_int8_threshold: 6.0
-- llm_int8_skip_modules: None
-- llm_int8_enable_fp32_cpu_offload: False
-- llm_int8_has_fp16_weight: False
-- bnb_4bit_quant_type: nf4
-- bnb_4bit_use_double_quant: False
-- bnb_4bit_compute_dtype: bfloat16
+<!-- 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
 
-dryanfurman at gmail
+[More Information Needed]
+
+
+## Training procedure
+
 
+### Framework versions
 
-## Framework versions
 
-- PEFT 0.6.0.dev0
+- PEFT 0.6.3.dev0

adapter_config.json

@@ -18,8 +18,8 @@
   "target_modules": [
     "q_proj",
     "k_proj",
-    "v_proj",
-    "o_proj"
+    "o_proj",
+    "v_proj"
   ],
   "task_type": "CAUSAL_LM"
 }

adapter_model.safetensors

@@ -1,3 +1,3 @@
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