Model Architecture: QwQ-32B-Preview
- Input: Text
- Output: Text
Model Optimizations:
- Weight quantization: FP8
- Activation quantization: FP8
Release Date: 3/1/2025
Version: 1.0
Model Developers: Neural Magic

Quantized version of QwQ-32B-Preview. It achieves an average score of 77.08 on the OpenLLM benchmark (version 1), whereas the unquantized model achieves 77.20.

Model Optimizations

This model was obtained by quantizing the weights and activations of QwQ-32B-Preview to FP8 data type, ready for inference with vLLM >= 0.5.2. 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.

Deployment

Use with vLLM

This model can be deployed efficiently using the vLLM backend, as shown in the example below.

python

from transformers import AutoTokenizer
from vllm import LLM, SamplingParams

max_model_len, tp_size = 4096, 1
model_name = "neuralmagic-ent/QwQ-32B-Preview-FP8-dynamic"
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 for more details.

Creation

This model was created with llm-compressor by running the code snippet below.

python

import argparse
from transformers import AutoModelForCausalLM, AutoTokenizer
from llmcompressor.modifiers.quantization import QuantizationModifier
from llmcompressor.transformers import oneshot
import os

def main():
    parser = argparse.ArgumentParser(description='Quantize a transformer model to FP8')
    parser.add_argument('--model_id', type=str, required=True,
                        help='The model ID from HuggingFace (e.g., "meta-llama/Meta-Llama-3-8B-Instruct")')
    parser.add_argument('--save_path', type=str, default='.',
                        help='Custom path to save the quantized model. If not provided, will use model_name-FP8-dynamic')
    args = parser.parse_args()

    # Load model
    model = AutoModelForCausalLM.from_pretrained(
        args.model_id, device_map="auto", torch_dtype="auto", trust_remote_code=True,
    )
    tokenizer = AutoTokenizer.from_pretrained(args.model_id)

    # Configure the quantization algorithm and scheme
    recipe = QuantizationModifier(
        targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"]
    )

    # Apply quantization
    oneshot(model=model, recipe=recipe)

    save_path = os.path.join(args.save_path, args.model_id.split("/")[1] + "-FP8-dynamic")
    os.makedirs(save_path, exist_ok=True)

    # Save to disk in compressed-tensors format
    model.save_pretrained(save_path)
    tokenizer.save_pretrained(save_path)
    print(f"Model and tokenizer saved to: {save_path}")

if __name__ == "__main__":
    main()

Evaluation

The model was evaluated on OpenLLM Leaderboard V1 and V2, using the following commands:

OpenLLM Leaderboard V1:

lm_eval \
  --model vllm \
  --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 \
  --tasks openllm \
  --write_out \
  --batch_size auto \
  --output_path output_dir \
  --show_config

OpenLLM Leaderboard V2:

lm_eval \
  --model vllm \
  --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 \
  --apply_chat_template \
  --fewshot_as_multiturn \
  --tasks leaderboard \
  --write_out \
  --batch_size auto \
  --output_path output_dir \
  --show_config

Accuracy

OpenLLM Leaderboard V1 evaluation scores

Metric	Qwen/QwQ-32B-Preview	neuralmagic-ent/QwQ-32B-Preview-FP8-dynamic
ARC-Challenge (Acc-Norm, 25-shot)	70.73	71.08
GSM8K (Strict-Match, 5-shot)	83.09	82.18
HellaSwag (Acc-Norm, 10-shot)	85.77	85.88
MMLU (Acc, 5-shot)	82.67	82.67
TruthfulQA (MC2, 0-shot)	60.88	60.54
Winogrande (Acc, 5-shot)	80.03	80.11
Average Score	77.20	77.08
Recovery	100.00	99.84

OpenLLM Leaderboard V2 evaluation scores

Metric	Qwen/QwQ-32B-Preview	neuralmagic-ent/QwQ-32B-Preview-FP8-dynamic
IFEval (Inst-and-Prompt Level Strict Acc, 0-shot)	42.34	40.48
BBH (Acc-Norm, 3-shot)	53.03	52.96
Math-Hard (Exact-Match, 4-shot)	21.15	20.82
GPQA (Acc-Norm, 0-shot)	2.97	1.99
MUSR (Acc-Norm, 0-shot)	9.57	10.93
MMLU-Pro (Acc, 5-shot)	52.00	51.62
Average Score	30.18	29.80
Recovery	100.00	98.74

RedHatAI/QwQ-32B-Preview-FP8-dynamic

QwQ-32B-Preview-FP8-dynamic

Model Overview