---
title: DeepSeek-R1-Distill-Qwen-32B-AWQ
canonical_url: "https://www.modelscope.cn/models/inarikami/DeepSeek-R1-Distill-Qwen-32B-AWQ"
md_url: "https://www.modelscope.cn/models/inarikami/DeepSeek-R1-Distill-Qwen-32B-AWQ.md"
repository: inarikami/DeepSeek-R1-Distill-Qwen-32B-AWQ
last_updated: 2025-02-20
license: mit
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - qwen2
architectures:
  - Qwen2ForCausalLM
base_model:
  - deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
base_model_relation: quantized
parameters: 5.7B
tensor_type:
  - F16
  - I32
  - BF16
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
inference_backends:
  - "deploy_task text/emb"
  - "lmdeploy 0.9.1"
  - "lmdeploy_turbomind 0.9.1"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 1483
stars: 3
tags:
  - code
  - deepseek_v3
  - qwen
  - int4
  - conversational
---

# DeepSeek-R1-Distill-Qwen-32B-AWQ

> DeepSeek-R1-Distill-Qwen-32B-AWQ - inarikami 在 ModelScope 开源的模型。DeepSeek-R1-Distill-Qwen-32B-AWQ wint4

inarikami/DeepSeek-R1-Distill-Qwen-32B-AWQ 是 ModelScope 魔搭社区上的 5.7B 参数text-generation模型，采用 mit 许可，基于 deepseek-ai/DeepSeek-R1-Distill-Qwen-32B 构建，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。

- **Repository**: inarikami/DeepSeek-R1-Distill-Qwen-32B-AWQ
- **License**: mit
- **Tasks**: text-generation
- **Parameters**: 5.7B
- **Base model**: deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
- **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Tags**: code, deepseek_v3, qwen, int4, conversational
- **Downloads**: 1483
- **Stars**: 3
- **Last updated**: 2025-02-20

Source: https://www.modelscope.cn/models/inarikami/DeepSeek-R1-Distill-Qwen-32B-AWQ

---

# DeepSeek-R1-Distill-Qwen-32B-AWQ wint4

Distillation of DeepSeek-R1 to Qwen 32B, quantized using AWQ to wint4. It fits on any 24GB VRAM GPU or 32GB URAM device!

## MMLU-PRO

The MMLU-PRO dataset evaluates subjects across 14 distinct fields using a 5-shot accuracy measurement. Each task assesses models following the methodology of the original MMLU implementation, with each having ten possible choices.

### Measure

- **Accuracy**: Evaluated as "exact_match"

### Shots

- **Shots**: 5-shot

### Tasks

| Tasks            | Filter        | n-shot | Metric      | Value  | Stderr |
|------------------|---------------|--------|-------------|--------|--------|
| mmlu_pro         | custom-extract|        | exact_match | 0.5875 | 0.0044 |
| biology          | custom-extract| 5      | exact_match | 0.7978 | 0.0150 |
| business         | custom-extract| 5      | exact_match | 0.5982 | 0.0175 |
| chemistry        | custom-extract| 5      | exact_match | 0.4691 | 0.0148 |
| computer_science | custom-extract| 5      | exact_match | 0.6122 | 0.0241 |
| economics        | custom-extract| 5      | exact_match | 0.7346 | 0.0152 |
| engineering      | custom-extract| 5      | exact_match | 0.3891 | 0.0157 |
| health           | custom-extract| 5      | exact_match | 0.6345 | 0.0168 |
| history          | custom-extract| 5      | exact_match | 0.6168 | 0.0249 |
| law              | custom-extract| 5      | exact_match | 0.4596 | 0.0150 |
| math             | custom-extract| 5      | exact_match | 0.6425 | 0.0130 |
| other            | custom-extract| 5      | exact_match | 0.6223 | 0.0160 |
| philosophy       | custom-extract| 5      | exact_match | 0.5731 | 0.0222 |
| physics          | custom-extract| 5      | exact_match | 0.5073 | 0.0139 |
| psychology       | custom-extract| 5      | exact_match | 0.7494 | 0.0154 |

### Groups

| Groups   | Filter        | n-shot | Metric      | Value  | Stderr |
|----------|---------------|--------|-------------|--------|--------|
| mmlu_pro | custom-extract|        | exact_match | 0.5875 | 0.0044 |