169 lines
7.7 KiB
Markdown
169 lines
7.7 KiB
Markdown
---
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tags:
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- fp8
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- vllm
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license: apache-2.0
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license_link: https://huggingface.co/Qwen/QwQ-32B-Preview/blob/main/LICENSE
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language:
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- en
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base_model: Qwen/Qwen2.5-32B-Instruct
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library_name: transformers
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---
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# QwQ-32B-Preview-FP8-dynamic
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## Model Overview
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- **Model Architecture:** QwQ-32B-Preview
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- **Input:** Text
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- **Output:** Text
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- **Model Optimizations:**
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- **Weight quantization:** FP8
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- **Activation quantization:** FP8
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- **Release Date:** 3/1/2025
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- **Version:** 1.0
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- **Model Developers:** Neural Magic
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Quantized version of [QwQ-32B-Preview](https://huggingface.co/Qwen/QwQ-32B-Preview).
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It achieves an average score of 77.08 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 77.20.
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### Model Optimizations
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This model was obtained by quantizing the weights and activations of [QwQ-32B-Preview](https://huggingface.co/Qwen/QwQ-32B-Preview) to FP8 data type, ready for inference with vLLM >= 0.5.2.
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This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. Only the weights and activations of the linear operators within transformers blocks are quantized.
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## Deployment
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### Use with vLLM
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This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
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```python
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from transformers import AutoTokenizer
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from vllm import LLM, SamplingParams
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max_model_len, tp_size = 4096, 1
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model_name = "neuralmagic-ent/QwQ-32B-Preview-FP8-dynamic"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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llm = LLM(model=model_name, tensor_parallel_size=tp_size, max_model_len=max_model_len, trust_remote_code=True)
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sampling_params = SamplingParams(temperature=0.3, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id])
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messages_list = [
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[{"role": "user", "content": "Who are you? Please respond in pirate speak!"}],
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]
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prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in messages_list]
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outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params)
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generated_text = [output.outputs[0].text for output in outputs]
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print(generated_text)
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```
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vLLM also supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
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## Creation
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This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below.
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```python
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import argparse
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from llmcompressor.modifiers.quantization import QuantizationModifier
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from llmcompressor.transformers import oneshot
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import os
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def main():
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parser = argparse.ArgumentParser(description='Quantize a transformer model to FP8')
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parser.add_argument('--model_id', type=str, required=True,
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help='The model ID from HuggingFace (e.g., "meta-llama/Meta-Llama-3-8B-Instruct")')
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parser.add_argument('--save_path', type=str, default='.',
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help='Custom path to save the quantized model. If not provided, will use model_name-FP8-dynamic')
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args = parser.parse_args()
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# Load model
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model = AutoModelForCausalLM.from_pretrained(
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args.model_id, device_map="auto", torch_dtype="auto", trust_remote_code=True,
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)
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tokenizer = AutoTokenizer.from_pretrained(args.model_id)
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# Configure the quantization algorithm and scheme
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recipe = QuantizationModifier(
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targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"]
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)
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# Apply quantization
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oneshot(model=model, recipe=recipe)
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save_path = os.path.join(args.save_path, args.model_id.split("/")[1] + "-FP8-dynamic")
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os.makedirs(save_path, exist_ok=True)
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# Save to disk in compressed-tensors format
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model.save_pretrained(save_path)
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tokenizer.save_pretrained(save_path)
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print(f"Model and tokenizer saved to: {save_path}")
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if __name__ == "__main__":
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main()
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```
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## Evaluation
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The model was evaluated on OpenLLM Leaderboard [V1](https://huggingface.co/spaces/open-llm-leaderboard-old/open_llm_leaderboard) and [V2](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/), using the following commands:
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OpenLLM Leaderboard V1:
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```
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lm_eval \
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--model vllm \
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--model_args pretrained="neuralmagic-ent/QwQ-32B-Preview-FP8-dynamic",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True \
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--tasks openllm \
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--write_out \
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--batch_size auto \
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--output_path output_dir \
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--show_config
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```
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OpenLLM Leaderboard V2:
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```
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lm_eval \
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--model vllm \
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--model_args pretrained="neuralmagic-ent/QwQ-32B-Preview-FP8-dynamic",dtype=auto,add_bos_token=False,max_model_len=4096,tensor_parallel_size=1,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True \
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--apply_chat_template \
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--fewshot_as_multiturn \
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--tasks leaderboard \
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--write_out \
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--batch_size auto \
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--output_path output_dir \
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--show_config
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```
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### Accuracy
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#### OpenLLM Leaderboard V1 evaluation scores
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| Metric | Qwen/QwQ-32B-Preview | neuralmagic-ent/QwQ-32B-Preview-FP8-dynamic |
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|-----------------------------------------|:---------------------------------:|:-------------------------------------------:|
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| ARC-Challenge (Acc-Norm, 25-shot) | 70.73 | 71.08 |
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| GSM8K (Strict-Match, 5-shot) | 83.09 | 82.18 |
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| HellaSwag (Acc-Norm, 10-shot) | 85.77 | 85.88 |
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| MMLU (Acc, 5-shot) | 82.67 | 82.67 |
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| TruthfulQA (MC2, 0-shot) | 60.88 | 60.54 |
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| Winogrande (Acc, 5-shot) | 80.03 | 80.11 |
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| **Average Score** | **77.20** | **77.08** |
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| **Recovery** | **100.00** | **99.84** |
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#### OpenLLM Leaderboard V2 evaluation scores
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| Metric | Qwen/QwQ-32B-Preview | neuralmagic-ent/QwQ-32B-Preview-FP8-dynamic |
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|---------------------------------------------------------|:---------------------------------:|:-------------------------------------------:|
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| IFEval (Inst-and-Prompt Level Strict Acc, 0-shot) | 42.34 | 40.48 |
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| BBH (Acc-Norm, 3-shot) | 53.03 | 52.96 |
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| Math-Hard (Exact-Match, 4-shot) | 21.15 | 20.82 |
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| GPQA (Acc-Norm, 0-shot) | 2.97 | 1.99 |
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| MUSR (Acc-Norm, 0-shot) | 9.57 | 10.93 |
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| MMLU-Pro (Acc, 5-shot) | 52.00 | 51.62 |
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| **Average Score** | **30.18** | **29.80** |
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| **Recovery** | **100.00** | **98.74** |
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