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--- |
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tags: |
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- w8a8 |
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- int8 |
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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-quantized.w8a8 |
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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:** INT8 |
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- **Activation quantization:** INT8 |
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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 76.49 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 INT8 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-quantized.w8a8" |
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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 with the following arguments: |
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```bash |
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python quantize.py --model_path Qwen/QwQ-32B-Preview --quant_path "output_dir/QwQ-32B-Preview-quantized.w8a8" --calib_size 1024 --dampening_frac 0.1 --observer mse |
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``` |
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```python |
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from datasets import load_dataset |
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from transformers import AutoTokenizer |
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from llmcompressor.modifiers.quantization import GPTQModifier |
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from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot, apply |
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import argparse |
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from compressed_tensors.quantization import QuantizationScheme, QuantizationArgs, QuantizationType, QuantizationStrategy |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--model_path', type=str) |
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parser.add_argument('--quant_path', type=str) |
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parser.add_argument('--calib_size', type=int, default=256) |
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parser.add_argument('--dampening_frac', type=float, default=0.1) |
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parser.add_argument('--observer', type=str, default="minmax") |
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args = parser.parse_args() |
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model = SparseAutoModelForCausalLM.from_pretrained( |
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args.model_path, |
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device_map="auto", |
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torch_dtype="auto", |
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use_cache=False, |
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trust_remote_code=True, |
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) |
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tokenizer = AutoTokenizer.from_pretrained(args.model_path) |
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NUM_CALIBRATION_SAMPLES = args.calib_size |
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DATASET_ID = "garage-bAInd/Open-Platypus" |
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DATASET_SPLIT = "train" |
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ds = load_dataset(DATASET_ID, split=DATASET_SPLIT) |
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ds = ds.shuffle(seed=42).select(range(NUM_CALIBRATION_SAMPLES)) |
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def preprocess(example): |
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concat_txt = example["instruction"] + "\n" + example["output"] |
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return {"text": concat_txt} |
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ds = ds.map(preprocess) |
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def tokenize(sample): |
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return tokenizer( |
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sample["text"], |
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padding=False, |
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truncation=False, |
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add_special_tokens=True, |
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) |
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ds = ds.map(tokenize, remove_columns=ds.column_names) |
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recipe = [ |
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GPTQModifier( |
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targets=["Linear"], |
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ignore=["lm_head"], |
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scheme="W8A8", |
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dampening_frac=args.dampening_frac, |
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observer=args.observer, |
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) |
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] |
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oneshot( |
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model=model, |
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dataset=ds, |
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recipe=recipe, |
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num_calibration_samples=args.calib_size, |
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max_seq_length=8192, |
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) |
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# Save to disk compressed. |
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SAVE_DIR = args.quant_path |
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model.save_pretrained(SAVE_DIR, save_compressed=True) |
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tokenizer.save_pretrained(SAVE_DIR) |
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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-quantized.w8a8",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-quantized.w8a8",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-quantized.w8a8 | |
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|-----------------------------------------|:---------------------------------:|:-------------------------------------------:| |
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| ARC-Challenge (Acc-Norm, 25-shot) | 70.73 | 70.73 | |
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| GSM8K (Strict-Match, 5-shot) | 83.09 | 79.91 | |
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| HellaSwag (Acc-Norm, 10-shot) | 85.77 | 85.75 | |
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| MMLU (Acc, 5-shot) | 82.67 | 82.24 | |
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| TruthfulQA (MC2, 0-shot) | 60.88 | 59.18 | |
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| Winogrande (Acc, 5-shot) | 80.03 | 81.14 | |
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| **Average Score** | **77.20** | **76.49** | |
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| **Recovery** | **100.00** | **99.08** | |
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#### OpenLLM Leaderboard V2 evaluation scores |
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| Metric | Qwen/QwQ-32B-Preview | neuralmagic-ent/QwQ-32B-Preview-quantized.w8a8 | |
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|---------------------------------------------------------|:---------------------------------:|:-------------------------------------------:| |
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| IFEval (Inst-and-Prompt Level Strict Acc, 0-shot) | 42.34 | 43.49 | |
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| BBH (Acc-Norm, 3-shot) | 53.03 | 52.95 | |
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| Math-Hard (Exact-Match, 4-shot) | 21.15 | 22.36 | |
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| GPQA (Acc-Norm, 0-shot) | 2.97 | 3.5 | |
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| MUSR (Acc-Norm, 0-shot) | 9.57 | 10.87 | |
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| MMLU-Pro (Acc, 5-shot) | 52.00 | 51.4 | |
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| **Average Score** | **30.18** | **30.76** | |
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| **Recovery** | **100.00** | **101.92** | |
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