README.md

---
tags:
- fp4
- vllm
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
pipeline_tag: text-generation
license: mit
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
---

# 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](https://huggingface.co/deepseek-ai/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](https://huggingface.co/deepseek-ai/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 75%.

Only the weights of the linear operators within transformers blocks are quantized using [LLM Compressor](https://github.com/vllm-project/llm-compressor).

## 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.
<details>
<summary>Model Usage Code</summary>
  
```python
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)
```
</details>

vLLM aslo supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.

## Creation

This model was created by applying [LLM Compressor with calibration samples from neuralmagic/calibration dataset](https://github.com/vllm-project/llm-compressor/blob/main/examples/multimodal_vision/llama4_example.py), as presented in the code snipet below.

<details>
<summary>Model Creation Code</summary>
  
```python

```
</details>

## Evaluation

This model was evaluated on the well-known OpenLLM v1 and HumanEval_64 benchmarks using [lm-evaluation-harness](https://github.com/neuralmagic/lm-evaluation-harness). The Reasoning evals were done using [ligheval](https://github.com/neuralmagic/lighteval).

### Accuracy

<table>
  <thead>
    <tr>
      <th>Category</th>
      <th>Metric</th>
      <th>DeepSeek-R1-Distill-Qwen-32B</th>
      <th>DeepSeek-R1-Distill-Qwen-32B NVFP4</th>
      <th>Recovery</th>
    </tr>
  </thead>
  <tbody>
    <!-- OpenLLM V1 -->
    <tr>
      <td rowspan="7"><b>OpenLLM V1</b></td>
      <td>arc_challenge</td>
      <td>63.48</td>
      <td>62.12</td>
      <td>97.86</td>
    </tr>
    <tr>
      <td>gsm8k</td>
      <td>86.88</td>
      <td>88.32</td>
      <td>101.66</td>
    </tr>
    <tr>
      <td>hellaswag</td>
      <td>83.51</td>
      <td>82.38</td>
      <td>98.65</td>
    </tr>
    <tr>
      <td>mmlu</td>
      <td>80.97</td>
      <td>80.42</td>
      <td>99.32</td>
    </tr>
    <tr>
      <td>truthfulqa_mc2</td>
      <td>56.82</td>
      <td>55.75</td>
      <td>98.12</td>
    </tr>
    <tr>
      <td>winogrande</td>
      <td>75.93</td>
      <td>75.14</td>
      <td>98.96</td>
    </tr>
    <tr>
      <td><b>Average</b></td>
      <td><b>74.60</b></td>
      <td><b>74.02</b></td>
      <td><b>99.23</b></td>
    </tr>
    <!-- Reasoning -->
    <tr>
      <td rowspan="4"><b>Reasoning</b></td>
      <td>AIME24 (0-shot)</td>
      <td>72.41</td>
      <td>62.07</td>
      <td>85.69</td>
    </tr>
    <tr>
      <td>AIME25 (0-shot)</td>
      <td>58.62</td>
      <td>62.07</td>
      <td>105.89</td>
    </tr>
    <tr>
      <td>GPQA (Diamond, 0-shot)</td>
      <td>68.02</td>
      <td>65.48</td>
      <td>96.27</td>
    </tr>
    <tr>
      <td><b>Average</b></td>
      <td><b>66.35</b></td>
      <td><b>63.21</b></td>
      <td><b>95.95</b></td>
    </tr>
    <!-- Coding -->
    <tr>
      <td rowspan="2"><b>Coding</b></td>
      <td>HumanEval_64 pass@2</td>
      <td>90.00</td>
      <td>89.32</td>
      <td>99.24</td>
    </tr>
  </tbody>
</table>



### Reproduction

The results were obtained using the following commands:

<details>
<summary>Model Evaluation Commands</summary>

#### 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
```


####  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_64_instruct \
  --batch_size auto
```

#### LightEval 
```
# --- model_args.yaml ---
cat > model_args.yaml <<'YAML'
model_parameters:
  model_name: "RedHatAI/DeepSeek-R1-Distill-Qwen-32B-NVFP4"
  dtype: auto
  gpu_memory_utilization: 0.9
  tensor_parallel_size: 2
  max_model_length: 40960
  generation_parameters:
    seed: 42
    temperature: 0.6
    top_k: 50
    top_p: 0.95
    min_p: 0.0
    max_new_tokens: 32768
YAML

lighteval vllm model_args.yaml \
  "lighteval|aime24|0,lighteval|aime25|0,lighteval|gpqa:diamond|0" \
  --max-samples -1 \
  --output-dir out_dir

```
</details>