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Model: okwinds/DeepSeek-R1-Distill-Qwen-7B-Int4-W4A16 Source: Original Platform
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README.md
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---
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frameworks:
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- Pytorch
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license: Apache License 2.0
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base_model:
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- deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
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tasks:
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- text-generation
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language:
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- en
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- cn
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tags:
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- Chat
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- Instruct
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- fine-tuned
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---
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# DeepSeek-R1-Distill-Qwen-7B-Int4-W4A16 量化高精校准
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### 原模型解读，请看公众号文章 👇🏻
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### <img src="https://www.modelscope.cn/datasets/okwinds/Human-Like-DPO-Dataset/resolve/master/wechat.png" width="30" height="30" align="absmiddle"> 觉察流 - [DeepSeek-R1挑战OpenAI，AI推理模型的新时代来临](https://mp.weixin.qq.com/s/mG18W607Xxt_lp_6NhNPAw)
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<span style="color:red">友情提醒，推理本模型时，请按照如下官方指引：</span>
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> 注意：建议在运行这些模型时设置适当的温度（0.5 到 0.7 之间），否则您可能会遇到无休止的重复或不连贯的输出问题。
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📖 关于量化损失方面的研究，可阅读公众号“觉察流”文章👇</br>
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《[Reason Model 的“瘦身计划”：量化技术的得与失](https://mp.weixin.qq.com/s/NMGq4UUkfo8GMix5LHnWCg)》
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#### _仓库作者在此 👇🏻 扫一扫_
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<img src="https://www.modelscope.cn/models/okwinds/GPT-2/resolve/master/qrcode_for_jcl_258.jpg" />
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_______________________________
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# 下载方法
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SDK下载
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```bash
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#安装ModelScope
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pip install modelscope
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```
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```python
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#SDK模型下载
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from modelscope import snapshot_download
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model_dir = snapshot_download('okwinds/DeepSeek-R1-Distill-Qwen-7B-Int4-W4A16')
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```
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Git下载
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```
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#Git模型下载
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git clone https://www.modelscope.cn/okwinds/DeepSeek-R1-Distill-Qwen-7B-Int4-W4A16.git
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```
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# 模型概述
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DeepSeek-R1-Distill-Qwen-7B-Int4-W4A16 是一个基于 [deepseek-ai/DeepSeek-R1-Distill-Qwen-7B](https://www.modelscope.cn/models/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) 的 Int4 量化的模型。
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- **模型名称:** DeepSeek-R1-Distill-Qwen-7B-Int4-W4A16
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- **模型架构:** Qwen2.5
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- **权重量化:** Int4
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## 量化模型
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该模型通过将 [deepseek-ai/DeepSeek-R1-Distill-Qwen-7B](https://www.modelscope.cn/models/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) 的权重量化为 Int4 数据类型而实现。
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量化过程将每个参数从 16bit 减少到 4bit，将模型占用磁盘空间大小，以及推理时加载模型需要的GPU显存空间，减少到了大约为原模型的1/4。只有 transformer 中的 Linear 层权重是量化的，其他层均保持为原模型数据类型，采用了混合精度的计算方式，尽可能减少模型量化后所带来的精度损失。
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量化本模型，使用了[AutoGPTQ](https://github.com/AutoGPTQ/AutoGPTQ)，采用 Symmetric group-wise 方式量化（group size : 128）。并在量化过程中，进行了数据校准，以提升模型生成精度。
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> <span style="color: red;">注意：本模型需要 compute capability > 8.0（Ampere、Ada Lovelace、Hopper 架构）的 Nvidia GPU 来支持 INT4 **混合精度**计算。</span>
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# 部署推理
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### 推荐使用 vLLM （ <span style="color: red;">version > 0.6.6.post1</span> ）
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Openai api 兼容模式
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```bash
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>>> vllm serve "/home/gavin/llm/DeepSeek-R1-Distill-Qwen-7B-Int4-W4A16" --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.9 --served-model-name "DeepSeek-R1-Distill-Qwen-7B-Int4-W4A16"
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```
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---
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以下引自原模型的Model Card
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## 1. 引言
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我们推出第一代推理模型 DeepSeek-R1-Zero 和 DeepSeek-R1。<br/>
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DeepSeek-R1-Zero 是一种通过大规模强化学习（Reinforcement Learning, RL）训练的模型，训练过程中未使用监督微调（Supervised Fine-Tuning, SFT）作为前置步骤，但在推理任务上表现出色。<br/>
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借助 RL，DeepSeek-R1-Zero 自然地展现出许多强大且有趣的推理行为。然而，DeepSeek-R1-Zero 也面临一些挑战，例如无休止的重复、可读性差以及语言混合等问题。<br/>
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为解决这些问题并进一步提升推理性能，我们推出了 DeepSeek-R1，该模型在 RL 之前引入了冷启动数据（cold-start data）。<br/>
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DeepSeek-R1 在数学、编程和推理任务上的表现与 OpenAI-o1 相当。<br/>
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为了支持研究社区，我们开源了 DeepSeek-R1-Zero、DeepSeek-R1，以及基于 Llama 和 Qwen 系列从 DeepSeek-R1 蒸馏出的六个Dense模型。<br/>
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其中，DeepSeek-R1-Distill-Qwen-32B 在多个基准测试中超越了 OpenAI-o1-mini，为密集模型创造了新的最佳性能。<br/>
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<p align="center">
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<img width="80%" src="https://www.modelscope.cn/api/v1/models/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B/repo?Revision=master&FilePath=figures%2Fbenchmark.jpg&View=true">
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</p>
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## 2. 模型概述
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后训练：在基础模型上进行大规模强化学习<br/>
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- 我们直接在基础模型上应用强化学习（RL），而不依赖于监督微调（SFT）作为前置步骤。
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这种方法使模型能够探索用于解决复杂问题的思维链（Chain-of-Thought, CoT），从而开发出 DeepSeek-R1-Zero。
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DeepSeek-R1-Zero 展示了自我验证、反思和生成长思维链的能力，这标志着研究社区的一个重要里程碑。
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值得注意的是，它是首次公开研究验证，通过纯 RL 激励大型语言模型（LLMs）的推理能力，无需 SFT。
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这一突破为该领域的未来发展铺平了道路。<br/>
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- 我们介绍了开发 DeepSeek-R1 的流程。该流程包括两个 RL 阶段，旨在发现改进的推理模式并符合人类偏好，以及两个 SFT 阶段，作为模型推理和非推理能力的种子。
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我们相信这一流程将有助于行业开发更好的模型。<br/>
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蒸馏：小模型也可以很强大<br/>
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- 我们证明了可以将大模型的推理模式蒸馏到小模型中，与通过小模型上的 RL 发现的推理模式相比，性能更好。开源的 DeepSeek-R1 及其 API 将有助于研究社区在未来蒸馏出更好的小模型。<br/>
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- 使用 DeepSeek-R1 生成的推理数据，我们微调了几个在研究社区中广泛使用的密集模型。评估结果表明，这些蒸馏后的小密集模型在基准测试中表现出色。我们基于 Qwen2.5 和 Llama3 系列开源了 1.5B、7B、8B、14B、32B 和 70B 的检查点，供社区使用。<br/>
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DeepSeek-R1-Distill 模型是基于开源模型微调的，使用 DeepSeek-R1 生成的样本进行训练。我们对其配置和分词器进行了小幅调整。请使用我们的设置运行这些模型。<br/>
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## 3. 评估结果
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DeepSeek-R1 评估<br/>
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对于所有模型，最大生成长度设置为 32,768 个Token。对于需要采样的基准测试，我们使用温度为 0.6、top-p 值为 0.95，并为每个查询生成 64 个响应以估计 pass@1。<br/>
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<div align="center">
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| Category | Benchmark (Metric) | Claude-3.5-Sonnet-1022 | GPT-4o 0513 | DeepSeek V3 | OpenAI o1-mini | OpenAI o1-1217 | DeepSeek R1 |
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|----------|-------------------|----------------------|------------|--------------|----------------|------------|--------------|
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| | Architecture | - | - | MoE | - | - | MoE |
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| | # Activated Params | - | - | 37B | - | - | 37B |
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| | # Total Params | - | - | 671B | - | - | 671B |
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| English | MMLU (Pass@1) | 88.3 | 87.2 | 88.5 | 85.2 | **91.8** | 90.8 |
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| | MMLU-Redux (EM) | 88.9 | 88.0 | 89.1 | 86.7 | - | **92.9** |
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| | MMLU-Pro (EM) | 78.0 | 72.6 | 75.9 | 80.3 | - | **84.0** |
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| | DROP (3-shot F1) | 88.3 | 83.7 | 91.6 | 83.9 | 90.2 | **92.2** |
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| | IF-Eval (Prompt Strict) | **86.5** | 84.3 | 86.1 | 84.8 | - | 83.3 |
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| | GPQA-Diamond (Pass@1) | 65.0 | 49.9 | 59.1 | 60.0 | **75.7** | 71.5 |
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| | SimpleQA (Correct) | 28.4 | 38.2 | 24.9 | 7.0 | **47.0** | 30.1 |
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| | FRAMES (Acc.) | 72.5 | 80.5 | 73.3 | 76.9 | - | **82.5** |
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| | AlpacaEval2.0 (LC-winrate) | 52.0 | 51.1 | 70.0 | 57.8 | - | **87.6** |
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| | ArenaHard (GPT-4-1106) | 85.2 | 80.4 | 85.5 | 92.0 | - | **92.3** |
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| Code | LiveCodeBench (Pass@1-COT) | 33.8 | 34.2 | - | 53.8 | 63.4 | **65.9** |
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| | Codeforces (Percentile) | 20.3 | 23.6 | 58.7 | 93.4 | **96.6** | 96.3 |
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| | Codeforces (Rating) | 717 | 759 | 1134 | 1820 | **2061** | 2029 |
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| | SWE Verified (Resolved) | **50.8** | 38.8 | 42.0 | 41.6 | 48.9 | 49.2 |
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| | Aider-Polyglot (Acc.) | 45.3 | 16.0 | 49.6 | 32.9 | **61.7** | 53.3 |
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| Math | AIME 2024 (Pass@1) | 16.0 | 9.3 | 39.2 | 63.6 | 79.2 | **79.8** |
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| | MATH-500 (Pass@1) | 78.3 | 74.6 | 90.2 | 90.0 | 96.4 | **97.3** |
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| | CNMO 2024 (Pass@1) | 13.1 | 10.8 | 43.2 | 67.6 | - | **78.8** |
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| Chinese | CLUEWSC (EM) | 85.4 | 87.9 | 90.9 | 89.9 | - | **92.8** |
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| | C-Eval (EM) | 76.7 | 76.0 | 86.5 | 68.9 | - | **91.8** |
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| | C-SimpleQA (Correct) | 55.4 | 58.7 | **68.0** | 40.3 | - | 63.7 |
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</div>
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蒸馏模型评估<br/>
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<div align="center">
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| Model | AIME 2024 pass@1 | AIME 2024 cons@64 | MATH-500 pass@1 | GPQA Diamond pass@1 | LiveCodeBench pass@1 | CodeForces rating |
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|------------------------------------------|------------------|-------------------|-----------------|----------------------|----------------------|-------------------|
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| GPT-4o-0513 | 9.3 | 13.4 | 74.6 | 49.9 | 32.9 | 759 |
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| Claude-3.5-Sonnet-1022 | 16.0 | 26.7 | 78.3 | 65.0 | 38.9 | 717 |
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| o1-mini | 63.6 | 80.0 | 90.0 | 60.0 | 53.8 | **1820** |
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| QwQ-32B-Preview | 44.0 | 60.0 | 90.6 | 54.5 | 41.9 | 1316 |
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| DeepSeek-R1-Distill-Qwen-1.5B | 28.9 | 52.7 | 83.9 | 33.8 | 16.9 | 954 |
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| DeepSeek-R1-Distill-Qwen-7B | 55.5 | 83.3 | 92.8 | 49.1 | 37.6 | 1189 |
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| DeepSeek-R1-Distill-Qwen-14B | 69.7 | 80.0 | 93.9 | 59.1 | 53.1 | 1481 |
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| DeepSeek-R1-Distill-Qwen-32B | **72.6** | 83.3 | 94.3 | 62.1 | 57.2 | 1691 |
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| DeepSeek-R1-Distill-Llama-8B | 50.4 | 80.0 | 89.1 | 49.0 | 39.6 | 1205 |
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| DeepSeek-R1-Distill-Llama-70B | 70.0 | **86.7** | **94.5** | **65.2** | **57.5** | 1633 |
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</div>
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## 4. 聊天网站和 API 平台
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您可以在 DeepSeek 官方网站 chat.deepseek.com 上与 DeepSeek-R1 进行对话，并打开 “深度思考” 按钮。
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我们还在 DeepSeek 平台提供 OpenAI 兼容的 API：platform.deepseek.com。
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## 5. 如何本地运行
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DeepSeek-R1 模型<br/>
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请访问 DeepSeek-V3 仓库，了解有关本地运行 DeepSeek-R1 的更多信息。<br/>
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DeepSeek-R1-Distill 模型<br/>
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DeepSeek-R1-Distill 模型的使用方式与 Qwen 或 Llama 模型相同。<br/>
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例如，您可以使用 vLLM 轻松启动服务：
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```Shell
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vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-7B --tensor-parallel-size 2 --max-model-len 32768 --enforce-eager
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```
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注意：我们建议在运行这些模型时设置适当的温度（0.5 到 0.7 之间），否则您可能会遇到无休止的重复或不连贯的输出问题。<br/>
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6. 许可<br/>
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本代码仓库和模型权重均在 MIT License 下授权。DeepSeek-R1 系列支持商业用途，允许进行任何修改和衍生作品，包括但不限于用于训练其他大型语言模型（LLMs）的蒸馏。请注意：<br/>
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- DeepSeek-R1-Distill-Qwen-1.5B、DeepSeek-R1-Distill-Qwen-7B、DeepSeek-R1-Distill-Qwen-14B 和 DeepSeek-R1-Distill-Qwen-32B 均基于 Qwen-2.5 系列，最初在 Apache 2.0 License 下授权，现已使用 DeepSeek-R1 精选的 800k 样本进行微调。<br/>
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- DeepSeek-R1-Distill-Llama-8B 基于 Llama3.1-8B-Base，最初在 llama3.1 许可证 下授权。<br/>
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- DeepSeek-R1-Distill-Llama-70B 基于 Llama3.3-70B-Instruct，最初在 llama3.3 许可证 下授权。<br/>
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69
config.json
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69
config.json
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{
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"_name_or_path": "okwinds/DeepSeek-R1-Distill-Qwen-7B-Int4-W4A16",
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"eos_token_id": 151643,
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"hidden_act": "silu",
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"hidden_size": 3584,
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"initializer_range": 0.02,
|
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}
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||||
}
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||||
3
model_407-00001-of-00002.safetensors
Normal file
3
model_407-00001-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:0d62254630aa4d3b2ea99e2da24903a96ffad142a088c0edd4eac5e6826a2f72
|
||||
size 4455349216
|
||||
3
model_407-00002-of-00002.safetensors
Normal file
3
model_407-00002-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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||||
oid sha256:7946740ab848b4a02904f05bff81211bf9600fc20bfad39bbcc8d1703a40ce1c
|
||||
size 1089994880
|
||||
23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<｜begin▁of▁sentence｜>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<｜end▁of▁sentence｜>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<｜end▁of▁sentence｜>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
757497
tokenizer.json
Normal file
757497
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
195
tokenizer_config.json
Normal file
195
tokenizer_config.json
Normal file
@@ -0,0 +1,195 @@
|
||||
{
|
||||
"add_bos_token": true,
|
||||
"add_eos_token": false,
|
||||
"add_prefix_space": null,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<｜end▁of▁sentence｜>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<｜User｜>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151645": {
|
||||
"content": "<｜Assistant｜>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151646": {
|
||||
"content": "<｜begin▁of▁sentence｜>",
|
||||
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|
||||
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|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|EOT|>",
|
||||
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|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151648": {
|
||||
"content": "<think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151649": {
|
||||
"content": "</think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
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|
||||
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|
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"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
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|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
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|
||||
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|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"151661": {
|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
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|
||||
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|
||||
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|
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"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"bos_token": "<｜begin▁of▁sentence｜>",
|
||||
"chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') %}{%- for message in messages %}{%- if message['role'] == 'system' %}{% set ns.system_prompt = message['content'] %}{%- endif %}{%- endfor %}{{bos_token}}{{ns.system_prompt}}{%- for message in messages %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{{'<｜User｜>' + message['content']}}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is none %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls']%}{%- if not ns.is_first %}{{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{%- set ns.is_first = true -%}{%- else %}{{'\\n' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}}{%- endif %}{%- endfor %}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is not none %}{%- if ns.is_tool %}{{'<｜tool▁outputs▁end｜>' + message['content'] + '<｜end▁of▁sentence｜>'}}{%- set ns.is_tool = false -%}{%- else %}{% set content = message['content'] %}{% if '</think>' in content %}{% set content = content.split('</think>')[-1] %}{% endif %}{{'<｜Assistant｜>' + content + '<｜end▁of▁sentence｜>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- set ns.is_output_first = false %}{%- else %}{{'\\n<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}{% endif %}{% if add_generation_prompt and not ns.is_tool %}{{'<｜Assistant｜><think>\\n'}}{% endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<｜end▁of▁sentence｜>",
|
||||
"extra_special_tokens": {},
|
||||
"legacy": true,
|
||||
"model_max_length": 16384,
|
||||
"pad_token": "<｜end▁of▁sentence｜>",
|
||||
"sp_model_kwargs": {},
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"unk_token": null,
|
||||
"use_default_system_prompt": false
|
||||
}
|
||||
Reference in New Issue
Block a user