---
title: DeepSeek-R1-Distill-Llama-70B-eval-result
canonical_url: "https://www.modelscope.cn/models/easyllm/DeepSeek-R1-Distill-Llama-70B-eval-result"
md_url: "https://www.modelscope.cn/models/easyllm/DeepSeek-R1-Distill-Llama-70B-eval-result.md"
repository: easyllm/DeepSeek-R1-Distill-Llama-70B-eval-result
chinese_name: "DeepSeek-R1-Distill-Llama-70B评测结果"
last_updated: 2025-04-29
pipeline_tag: text-generation
tasks:
- text-generation
model_type:
- llama
architectures:
- LlamaForCausalLM
library_name:
- 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: 134
stars: 0
---
# DeepSeek-R1-Distill-Llama-70B-eval-result
> DeepSeek-R1-Distill-Llama-70B-eval-result - easyllm 在 ModelScope 开源的模型。DeepSeek-R1-Distill-Llama-70B评测结果: 8大领域，300多项细分能力评测。
easyllm/DeepSeek-R1-Distill-Llama-70B-eval-result 是 ModelScope 魔搭社区上的text-generation模型，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。
- **Repository**: easyllm/DeepSeek-R1-Distill-Llama-70B-eval-result
- **Tasks**: text-generation
- **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Downloads**: 134
- **Stars**: 0
- **Last updated**: 2025-04-29
Source: https://www.modelscope.cn/models/easyllm/DeepSeek-R1-Distill-Llama-70B-eval-result
---
# 模型信息
We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1.
DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrated remarkable performance on reasoning.
With RL, DeepSeek-R1-Zero naturally emerged with numerous powerful and interesting reasoning behaviors.
However, DeepSeek-R1-Zero encounters challenges such as endless repetition, poor readability, and language mixing. To address these issues and further enhance reasoning performance,
we introduce DeepSeek-R1, which incorporates cold-start data before RL.
DeepSeek-R1 achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks.
To support the research community, we have open-sourced DeepSeek-R1-Zero, DeepSeek-R1, and six dense models distilled from DeepSeek-R1 based on Llama and Qwen. DeepSeek-R1-Distill-Qwen-32B outperforms OpenAI-o1-mini across various benchmarks, achieving new state-of-the-art results for dense models.
---
**Post-Training: Large-Scale Reinforcement Learning on the Base Model**
- We directly apply reinforcement learning (RL) to the base model without relying on supervised fine-tuning (SFT) as a preliminary step. This approach allows the model to explore chain-of-thought (CoT) for solving complex problems, resulting in the development of DeepSeek-R1-Zero. DeepSeek-R1-Zero demonstrates capabilities such as self-verification, reflection, and generating long CoTs, marking a significant milestone for the research community. Notably, it is the first open research to validate that reasoning capabilities of LLMs can be incentivized purely through RL, without the need for SFT. This breakthrough paves the way for future advancements in this area.
- We introduce our pipeline to develop DeepSeek-R1. The pipeline incorporates two RL stages aimed at discovering improved reasoning patterns and aligning with human preferences, as well as two SFT stages that serve as the seed for the model's reasoning and non-reasoning capabilities.
We believe the pipeline will benefit the industry by creating better models.
---
**Distillation: Smaller Models Can Be Powerful Too**
- We demonstrate that the reasoning patterns of larger models can be distilled into smaller models, resulting in better performance compared to the reasoning patterns discovered through RL on small models. The open source DeepSeek-R1, as well as its API, will benefit the research community to distill better smaller models in the future.
- Using the reasoning data generated by DeepSeek-R1, we fine-tuned several dense models that are widely used in the research community. The evaluation results demonstrate that the distilled smaller dense models perform exceptionally well on benchmarks. We open-source distilled 1.5B, 7B, 8B, 14B, 32B, and 70B checkpoints based on Qwen2.5 and Llama3 series to the community.
### DeepSeek-R1 Models
| **Model** | **#Total Params** | **#Activated Params** | **Context Length** |
| :------------: | :------------: | :------------: | :------------: |
| DeepSeek-R1-Zero | 671B | 37B | 128K |
| DeepSeek-R1 | 671B | 37B | 128K |
DeepSeek-R1-Zero & DeepSeek-R1 are trained based on DeepSeek-V3-Base.
For more details regarding the model architecture, please refer to [DeepSeek-V3](https://github.com/deepseek-ai/DeepSeek-V3) repository.
### DeepSeek-R1-Distill Models
| **Model** | **Base Model** |
| :------------: | :------------: |
| DeepSeek-R1-Distill-Qwen-1.5B | [Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B) |
| DeepSeek-R1-Distill-Qwen-7B | [Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) |
| DeepSeek-R1-Distill-Llama-8B | [Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) |
| DeepSeek-R1-Distill-Qwen-14B | [Qwen2.5-14B](https://huggingface.co/Qwen/Qwen2.5-14B) |
|DeepSeek-R1-Distill-Qwen-32B | [Qwen2.5-32B](https://huggingface.co/Qwen/Qwen2.5-32B) |
| DeepSeek-R1-Distill-Llama-70B | [Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) |
# 模型下载
[点击下载](https://modelscope.cn/models/deepseek-ai/DeepSeek-R1-Distill-Llama-70B/files)
# 评测结果
### 开源模型对比
|排名|大模型|机构|size|总分| |医疗|教育|金融|法律|行政公务|心理健康|推理与数学计算|语言与指令遵从|
|---|-----|---|-------|---|-|----|---|---|---|------|-------|-----------|------------|
|1|***DeepSeek-R1***|深度求索|671B|80.8| |81.9|89.2|82.9|73.8|83.0|61.5|89.2|84.8|
|2|hunyuan-large|腾讯|389B|80.2| | 88.6|85.5|84.6|82.4| 70.4|73.2| 76.8|80.1|
|3|***deepseek-chat-v3-0324(new)***|深度求索|671B|77.7| |78.0|87.0|76.1|61.7|81.2|64.6|87.6|85.4|
|4|qwq-32b|阿里巴巴|32B|77.5| | 76.5|87.2|78.3|60.9| 82.2|63.0| 87.2|85.2|
|5|GLM-Z1-32B-0414(new)|智谱AI|32B|75.6| | 72.6|81.9|74.8|62.2| 80.0|63.3| 88.5|81.3|
|6|***DeepSeek-R1-Distill-Qwen-32B***|深度求索|32B|71.8| |69.4|85.1|73.8|51.8|76.0|53.8|83.6|81.4|
|7|GLM-4-32B-0414(new)|智谱AI|32B|71.8| | 71.7|76.5|72.3|54.5| 76.0|60.9| 81.2|81.3|
|8|Llama-4-Maverick-17B-128E-Instruct-FP8(new)|Meta|400B|71.3| | 75.8|83.0|71.1|48.1| 69.0|59.0| 82.7|81.6|
|9|qwen2.5-72b-instruct|阿里巴巴|72B|71.2| | 74.1|80.0|75.6|51.6| 67.0|59.5| 79.7|81.7|
|10|MiniMax-Text-01|minimax|456B|69.7| | 70.6|79.8|70.0|50.7| 71.0|57.8| 76.0|81.3|
|11|qwen2.5-32b-instruct|阿里巴巴|32B|69.1| | 71.6|82.0|71.8|50.8| 64.5|57.8| 73.5|80.7|
|12|***DeepSeek-R1-Distill-Qwen-14B***|深度求索|14B|66.5| |62.8|79.3|67.5|40.2|66.5|55.6|81.0|78.7|
|13|qwen2.5-14b-instruct|阿里巴巴|14B|65.9| | 67.7|78.6|68.0|45.4| 61.0|56.1| 70.4|79.9|
|14|GLM-Z1-9B-0414(new)|智谱AI|9B|63.2| | 58.0|66.9|63.8|31.5| 75.5|48.8| 84.8|76.2|
|15|qwen2.5-7b-instruct|阿里巴巴|7B|62.5| | 64.5|74.8|66.5|41.8| 53.0|56.0| 67.6|76.1|
|16|Llama-4-Scout-17B-16E-Instruct(new)|Meta|109B|62.4| | 65.4|78.0|62.1|31.8| 55.5|54.0| 76.0|76.2|
|**17**|**DeepSeek-R1-Distill-Llama-70B**|**深度求索**|**70B**|**62.2**| |**54.1**|**72.9**|**60.6**|**34.7**|**70.0**|**46.2**|**81.8**|**77.0**|
|18|internlm2_5-20b-chat|上海人工智能实验室|20B|61.6| | 65.4|73.9|62.9|44.7| 53.5|52.7| 62.4|77.2|
|19|Meta-Llama-3.1-405B-Instruct|Meta|405B|61.0| | 66.1|64.1|59.3|34.7| 59.0|53.9| 73.2|77.9|
|20|internlm2_5-7b-chat|上海人工智能实验室|7B|60.6| | 64.8|66.1|64.1|43.1| 55.5|51.0| 64.3|76.3|
|21|GLM-4-9B-0414(new)|智谱AI|9B|60.3| | 59.3|72.0|62.1|40.8| 53.5|49.2| 68.3|76.8|
|22|Llama-3.3-70B-Instruct|Meta|70B|59.0| | 59.2|63.8|56.9|29.9| 60.5|49.6| 74.2|78.0|
|23|Mistral-Small-3.1-24B-Instruct-2503(new)|Mistral|24B|58.7| | 53.1|68.0|57.6|33.3| 57.0|47.1| 76.3|77.1|
|24|Llama-3.3-70B-Instruct-fp8|Meta|70B|58.4| | 58.7|63.0|56.0|29.2| 59.0|48.5| 74.7|78.1|
|25|GLM-Z1-Rumination-32B-0414(new)|智谱AI|32B|57.5| | 47.9|69.6|54.3|38.9| 56.7|44.1| 78.3|70.2|
|26|Hermes-3-Llama-3.1-405B|NousResearch|405B|57.4| | 55.6|62.3|59.2|29.8| 52.5|48.9| 72.7|78.0|
|27|glm-4-9b-chat|智谱AI|9B|56.8| | 56.0|73.4|57.3|36.5| 52.0|47.1| 60.1|71.7|
|28|gemma-3-27b-it|Google|27B|56.4| | 48.2|64.3|56.1|21.3| 66.5|44.5| 77.4|73.1|
|29|phi-4|微软|14B|55.3| | 43.4|59.7|56.0|24.9| 64.5|43.2| 77.3|73.7|
|30|Mistral-Small-24B-Instruct-2501|Mistral|24B|53.2| | 45.8|60.5|53.1|26.3| 53.5|40.2| 71.4|74.4|
|31|gemma-3-12b-it|Google|12B|52.2| | 43.6|58.2|46.6|20.4| 59.0|41.1| 77.3|71.6|
|32|qwen2.5-3b-instruct|阿里巴巴|3B|50.6| | 49.8|62.4|53.8|26.9| 42.0|43.8| 59.1|67.6|
|33|***DeepSeek-R1-Distill-Qwen-7B***|深度求索|7B|45.4| |28.8|52.8|45.8|19.2|54.0|30.4|70.9|61.2|
|34|***DeepSeek-R1-Distill-Llama-8B***|深度求索|8B|45.2| |29.7|51.4|42.6|20.3|52.0|31.9|68.5|64.8|
|35|Llama-3.1-8B-Instruct|Meta|8B|44.0| | 39.9|44.3|40.9|21.3| 43.0|37.2| 59.9|65.8|
|36|Meta-Llama-3.1-8B-Instruct-fp8|Meta|8B|42.9| | 39.2|43.0|40.8|19.5| 37.5|34.0| 63.1|65.7|
|37|qwen2.5-1.5b-instruct|阿里巴巴|2B|42.3| | 43.8|56.0|48.6|25.6| 35.0|39.6| 38.5|51.5|
|38|gemma-3-4b-it|Google|4B|41.1| | 31.8|46.6|39.7|16.6| 39.5|29.2| 67.6|58.0|
|39|Llama-3.2-3B-Instruct|Meta|3B|38.5| | 34.6|37.5|35.0|17.1| 34.0|29.6| 57.6|62.7|
|40|***DeepSeek-R1-Distill-Qwen-1.5B***|深度求索|2B|35.5| |23.8|42.8|34.5|15.4|34.5|23.9|61.8|47.1|
|41|Llama-3.2-1B-Instruct|Meta|1B|30.1| | 25.4|28.3|28.8|14.6| 29.0|21.1| 41.6|51.9|
|42|qwen2.5-0.5b-instruct|阿里巴巴|1B|30.0| | 29.2|36.8|33.9|18.4| 22.5|24.5| 35.2|39.2|
|43|gemma-3-1b-it|Google|1B|28.6| | 23.6|31.9|28.7|15.4| 29.0|20.6| 31.5|48.2|
### 精益评测：更细分的维度
- 医疗
- 医师
- 护理
- 药师
- 医技
- 医学基础知识
- 医学考研
- 教育
- 小学学科
- 初中学科
- 中考
- 高中学科
- 高考
- 高等教育
- 考研
- 教师资格
- 金融排行榜
- 财务
- 银行
- 保险
- 证券
- 其他金融资格考试
- 金融基础知识
- 金融应用
- 法律排行榜
- 律师资格考试
- 行政公务排行榜
- 公务员考试
- 心理健康排行榜
- 推理与数学计算排行榜
- 演绎推理
- 常识推理
- 符号推理BBH
- 算术能力
- 表格问答
- 高中奥数
- 初中奥数
- 小学奥数
- 语言与指令遵从排行榜
- 成语理解
- 情感分析
- 文本蕴含
- 分类能力
- 信息抽取
- 阅读理解
- C3中文阅读理解
- 代词理解CLUEWSC
- 诗词匹配CCPM
- 中文指令遵从
以上细分能力评测结果，详见[github](https://github.com/jeinlee1991/chinese-llm-benchmark)