DeepSeek-R1-Distill-Qwen-32B-NVFP4

Model Overview

• Model Architecture: DeepSeek-R1-Distill-Qwen-32B
  • Input: Text / Image
  • Output: Text
• Model Optimizations:
  • Weight quantization: FP4
  • Activation quantization: FP4
• Release Date: 7/30/25
• Version: 1.0
• Model Developers: RedHatAI

This model is a quantized version of DeepSeek-R1-Distill-Qwen-32B. It was evaluated on a several tasks to assess the its quality in comparison to the unquatized model.

Model Optimizations

This model was obtained by quantizing the weights and activations of DeepSeek-R1-Distill-Qwen-32B to FP4 data type, ready for inference with vLLM>=0.9.1 This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 25%.

Only the weights of the linear operators within transformers blocks are quantized using LLM Compressor.

Deployment

Use with vLLM

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

Model Usage Code

from vllm import LLM, SamplingParams
from transformers import AutoTokenizer

model_id = "RedHatAI/DeepSeek-R1-Distill-Qwen-32B-NVFP4"
number_gpus = 2

sampling_params = SamplingParams(temperature=0.6, top_p=0.9, max_tokens=256)

tokenizer = AutoTokenizer.from_pretrained(model_id)

messages = [
    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
    {"role": "user", "content": "Who are you?"},
]

prompts = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)

llm = LLM(model=model_id, tensor_parallel_size=number_gpus)

outputs = llm.generate(prompts, sampling_params)

generated_text = outputs[0].outputs[0].text
print(generated_text)

vLLM aslo supports OpenAI-compatible serving. See the documentation for more details.

Creation

This model was created by applying LLM Compressor with calibration samples from neuralmagic/calibration dataset, as presented in the code snipet below.

Model Creation Code

Evaluation

This model was evaluated on the well-known OpenLLM v1, OpenLLM v2, HumanEval, and HumanEval_64 benchmarks. All evaluations were conducted using lm-evaluation-harness.

Category	Metric	DeepSeek-R1-Distill-Qwen-32B	DeepSeek-R1-Distill-Qwen-32B-NVFP4	Recovery (%)
OpenLLM V1	ARC Challenge	67.66	64.25	94.94%
	GSM8K	83.02	84.84	102.19%
	Hellaswag	83.79	83.28	99.39%
	MMLU	81.25	80.79	99.43%
	TruthfulQA-mc2	58.37	57.50	98.51%
	Winogrande	75.77	76.40	100.83%
	Average	74.98	74.51	99.38%
OpenLLM V2	MMLU-Pro			%
	IFEval			%
	BBH			%
	Math-Hard			%
	GPQA			%
	MuSR			%
	Average			%
Reasoning	Math 500	95.09	95.60	100.54%
	GPQA (diamond)	64.05	61.11	95.41%
	AIME25	69.75 (AIME24)	53.33	76.45%
	LCB: Code Generation	–	54.29	–
Coding	HumanEval Instruct pass@1	–	–	–
	HumanEval 64 Instruct pass@2	–	–	–
	HumanEval 64 Instruct pass@8	–	–	–
	HumanEval 64 Instruct pass@16	–	–	–
	HumanEval 64 Instruct pass@32	–	–	–
	HumanEval 64 Instruct pass@64	–	–	–

Reproduction

The results were obtained using the following commands:

Model Evaluation Commands

OpenLLM v1

lm_eval \
  --model vllm \
  --model_args pretrained="RedHatAI/DeepSeek-R1-Distill-Qwen-32B-NVFP4",dtype=auto,max_model_len=4096,tensor_parallel_size=2,enable_chunked_prefill=True,enforce_eager=True\
  --apply_chat_template \
  --fewshot_as_multiturn \
  --tasks openllm \
  --batch_size auto

OpenLLM v2

lm_eval \
  --model vllm \
  --model_args pretrained="RedHatAI/DeepSeek-R1-Distill-Qwen-32B-NVFP4",dtype=auto,max_model_len=15000,tensor_parallel_size=2,enable_chunked_prefill=True,enforce_eager=True\
  --apply_chat_template \
  --fewshot_as_multiturn \
  --tasks leaderboard \
  --batch_size auto

HumanEval and HumanEval_64

lm_eval \
  --model vllm \
  --model_args pretrained="RedHatAI/DeepSeek-R1-Distill-Qwen-32B-NVFP4",dtype=auto,max_model_len=4096,tensor_parallel_size=2,enable_chunked_prefill=True,enforce_eager=True\
  --apply_chat_template \
  --fewshot_as_multiturn \
  --tasks humaneval_instruct \
  --batch_size auto


lm_eval \
  --model vllm \
  --model_args pretrained="RedHatAI/DeepSeek-R1-Distill-Qwen-32B-NVFP4",dtype=auto,max_model_len=4096,tensor_parallel_size=2,enable_chunked_prefill=True,enforce_eager=True\
  --apply_chat_template \
  --fewshot_as_multiturn \
  --tasks humaneval_64_instruct \
  --batch_size auto