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README.md

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+---
+language:
+- en
+tags:
+- moe
+- int4
+- w4a16
+- vllm
+license: other
+license_name: deepseek
+license_link: https://github.com/deepseek-ai/DeepSeek-V2/blob/main/LICENSE-MODEL
+library_name: transformers
+---
+
+# DeepSeek-V2.5-1210-quantized.w4a16
+
+## Model Overview
+- **Model Architecture:** DeepSeek-V2.5-1210
+  - **Input:** Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Weight quantization:** INT4
+  - **Activation quantization:** None
+- **Release Date:** 3/1/2025
+- **Version:** 1.0
+- **Model Developers:** Neural Magic
+
+Quantized version of [DeepSeek-V2.5-1210](https://huggingface.co/deepseek-ai/DeepSeek-V2.5-1210).
+It achieves an average score of 77.2 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 77.82.
+
+### Model Optimizations
+
+This model was obtained by quantizing only the weights to INT4 data type, ready for inference with vLLM >= 0.5.2.
+This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%. The weights of the linear operators within transformers blocks are quantized, except the MLP routers. 
+
+## Deployment
+
+### Use with vLLM
+
+This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
+
+```python
+from transformers import AutoTokenizer
+from vllm import LLM, SamplingParams
+
+max_model_len, tp_size = 4096, 2
+model_name = "neuralmagic-ent/DeepSeek-V2.5-1210-quantized.w4a16"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+llm = LLM(model=model_name, tensor_parallel_size=tp_size, max_model_len=max_model_len, trust_remote_code=True)
+sampling_params = SamplingParams(temperature=0.3, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id])
+
+messages_list = [
+    [{"role": "user", "content": "Who are you? Please respond in pirate speak!"}],
+]
+
+prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in messages_list]
+
+outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params)
+
+generated_text = [output.outputs[0].text for output in outputs]
+print(generated_text)
+```
+
+vLLM also supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
+
+## Creation
+
+This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below with the following command:
+
+```bash
+python quantize.py --model_path deepseek-ai/DeepSeek-V2.5-1210 --quant_path "output_dir" --calib_size 256 --dampening_frac 0.1 --observer minmax --actorder weight
+```
+
+
+```python
+`from datasets import load_dataset
+from transformers import AutoTokenizer
+from llmcompressor.modifiers.quantization import GPTQModifier
+from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot, apply
+import argparse
+from compressed_tensors.quantization import QuantizationScheme, QuantizationArgs, QuantizationType, QuantizationStrategy
+from llmcompressor.transformers.compression.helpers import calculate_offload_device_map
+import torch
+
+
+def parse_actorder(value):
+    # Interpret the input value for --actorder
+    if value.lower() == "false":
+        return False
+    elif value.lower() == "weight":
+        return "weight"
+    elif value.lower() == "group":
+        raise ValueError("group not supported for TP>1 and MoEs")
+    else:
+        raise argparse.ArgumentTypeError("Invalid value for --actorder. Use 'group' or 'False'.")
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--model_path', type=str)
+parser.add_argument('--quant_path', type=str)
+parser.add_argument('--num_bits', type=int, default=4)
+parser.add_argument('--sequential_update', type=bool, default=True)
+parser.add_argument('--calib_size', type=int, default=256)
+parser.add_argument('--dampening_frac', type=float, default=0.05)
+parser.add_argument('--observer', type=str, default="minmax")
+parser.add_argument(
+    '--actorder',
+    type=parse_actorder,
+    default=False,  # Default value is False
+    help="Specify actorder as 'group' (string) or False (boolean)."
+)
+
+args = parser.parse_args()
+
+device_map = calculate_offload_device_map(
+    args.model_path,
+    reserve_for_hessians=True,
+    num_gpus=torch.cuda.device_count(),
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True,
+)
+
+model = SparseAutoModelForCausalLM.from_pretrained(
+    args.model_path,
+    device_map=device_map,
+    torch_dtype=torch.bfloat16,
+    use_cache=False,
+    trust_remote_code=True,
+)
+tokenizer = AutoTokenizer.from_pretrained(args.model_path)
+
+NUM_CALIBRATION_SAMPLES = args.calib_size
+DATASET_ID = "garage-bAInd/Open-Platypus"
+DATASET_SPLIT = "train"
+ds = load_dataset(DATASET_ID, split=DATASET_SPLIT)
+ds = ds.shuffle(seed=42).select(range(NUM_CALIBRATION_SAMPLES))
+
+def preprocess(example):
+    concat_txt = example["instruction"] + "\n" + example["output"]
+    return {"text": concat_txt}
+
+ds = ds.map(preprocess)
+
+def tokenize(sample):
+    return tokenizer(
+        sample["text"],
+        padding=False,
+        truncation=False,
+        add_special_tokens=True,
+    )
+
+
+ds = ds.map(tokenize, remove_columns=ds.column_names)
+
+quant_scheme = QuantizationScheme(
+    targets=["Linear"],
+    weights=QuantizationArgs(
+        num_bits=args.num_bits,
+        type=QuantizationType.INT,
+        symmetric=True,
+        group_size=128,
+        strategy=QuantizationStrategy.GROUP,
+        observer=args.observer,
+        actorder=args.actorder
+    ),
+    input_activations=None,
+    output_activations=None,
+)
+
+recipe = [
+    GPTQModifier(
+        targets=["Linear"],
+        ignore=["lm_head", "re:.*\.mlp\.gate$"],
+        sequential_update=args.sequential_update,
+        dampening_frac=args.dampening_frac,
+        config_groups={"group_0": quant_scheme},
+    )
+]
+oneshot(
+    model=model,
+    dataset=ds,
+    recipe=recipe,
+    num_calibration_samples=args.calib_size,
+)
+
+# Save to disk compressed.
+SAVE_DIR = args.quant_path
+model.save_pretrained(SAVE_DIR, save_compressed=True, skip_compression_stats=True)
+tokenizer.save_pretrained(SAVE_DIR)
+```
+
+## Evaluation
+
+The model was evaluated on OpenLLM Leaderboard [V1](https://huggingface.co/spaces/open-llm-leaderboard-old/open_llm_leaderboard) using the following command:
+
+OpenLLM Leaderboard V1:
+```
+lm_eval \
+  --model vllm \
+  --model_args pretrained="neuralmagic-ent/DeepSeek-V2.5-1210-quantized.w4a16",dtype=float16,add_bos_token=True,max_model_len=4096,tensor_parallel_size=4,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True \
+  --tasks openllm \
+  --write_out \
+  --batch_size auto \
+  --output_path output_dir \
+  --show_config
+```
+
+
+### Accuracy
+
+#### OpenLLM Leaderboard V1 evaluation scores
+
+| Metric                                   | deepseek-ai/DeepSeek-V2.5-1210             | neuralmagic-ent/DeepSeek-V2.5-1210-quantized.w4a16 |
+|-----------------------------------------|:---------------------------------:|:-------------------------------------------:|
+| ARC-Challenge (Acc-Norm, 25-shot)       | 72.61                            | 72.18                                       |
+| GSM8K (Strict-Match, 5-shot)            | 88.25                            | 88.10                                       |
+| HellaSwag (Acc-Norm, 10-shot)           | 85.01                            | 83.48                                       |
+| MMLU (Acc, 5-shot)                      | 79.60                            | 78.36                                       |
+| TruthfulQA (MC2, 0-shot)                | 57.18                            | 57.01                                       |
+| Winogrande (Acc, 5-shot)                | 84.29                            | 84.06                                       |
+| **Average Score**                       | **77.82**                        | **77.20**                                   |
+| **Recovery**                            | **100.00**                       | **99.20**                                   |
+