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@ -79,7 +79,7 @@ We pretrained DeepSeek-V2 on a diverse and high-quality corpus comprising 8.1 tr
| **Model** | **Context Length** | **Download** |
| :------------: | :------------: | :------------: |
| DeepSeek-V2 | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V2) |
| DeepSeek-V2-Chat(RL) | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V2-Chat) |
| DeepSeek-V2-Chat (RL) | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V2-Chat) |
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@ -91,7 +91,7 @@ Due to the constraints of HuggingFace, the open-source code currently experience
<div align="center">
| **Benchmark** | **Domain** | **LLaMA3 70B** | **Mixtral 8x22B** | **DeepSeek V1 (Dense-67B)** | **DeepSeek V2 (MoE-236B)** |
| **Benchmark** | **Domain** | **LLaMA3 70B** | **Mixtral 8x22B** | **DeepSeek-V1 (Dense-67B)** | **DeepSeek-V2 (MoE-236B)** |
|:-----------:|:--------:|:------------:|:---------------:|:-------------------------:|:------------------------:|
| **MMLU** | English | 78.9 | 77.6 | 71.3 | 78.5 |
| **BBH** | English | 81.0 | 78.9 | 68.7 | 78.9 |
@ -116,7 +116,7 @@ Evaluation results on the ``Needle In A Haystack`` (NIAH) tests. DeepSeek-V2 pe
#### Standard Benchmark
<div align="center">
| Benchmark | Domain | QWen1.5 72B Chat | Mixtral 8x22B | LLaMA3 70B Instruct | DeepSeek V1 Chat (SFT) | DeepSeek V2 Chat(SFT) | DeepSeek V2 Chat(RL) |
| Benchmark | Domain | QWen1.5 72B Chat | Mixtral 8x22B | LLaMA3 70B Instruct | DeepSeek-V1 Chat (SFT) | DeepSeek-V2 Chat (SFT) | DeepSeek-V2 Chat (RL) |
|:-----------:|:----------------:|:------------------:|:---------------:|:---------------------:|:-------------:|:-----------------------:|:----------------------:|
| **MMLU** | English | 76.2 | 77.8 | 80.3 | 71.1 | 78.4 | 77.8 |
| **BBH** | English | 65.9 | 78.4 | 80.1 | 71.7 | 81.3 | 79.7 |
@ -143,15 +143,15 @@ We evaluate our model on AlpacaEval 2.0 and MTBench, showing the competitive per
| **模型** | **开源/闭源** | **总分** | **中文推理** | **中文语言** |
| :---: | :---: | :---: | :---: | :---: |
| gpt-4-1106-preview | 闭源 | 8.01 | 7.73 | 8.29 |
| DeepSeek-V2 Chat(RL) | 开源 | 7.91 | 7.45 | 8.36 |
| erniebot-4.0-202404(文心一言) | 闭源 | 7.89 | 7.61 | 8.17 |
| DeepSeek-V2 Chat(SFT) | 开源 | 7.74 | 7.30 | 8.17 |
| DeepSeek-V2 Chat (RL) | 开源 | 7.91 | 7.45 | 8.36 |
| erniebot-4.0-202404 (文心一言) | 闭源 | 7.89 | 7.61 | 8.17 |
| DeepSeek-V2 Chat (SFT) | 开源 | 7.74 | 7.30 | 8.17 |
| gpt-4-0613 | 闭源 | 7.53 | 7.47 | 7.59 |
| erniebot-4.0-202312(文心一言) | 闭源 | 7.36 | 6.84 | 7.88 |
| moonshot-v1-32k-202404(月之暗面) | 闭源 | 7.22 | 6.42 | 8.02 |
| Qwen1.5-72B-Chat(通义千问) | 开源 | 7.19 | 6.45 | 7.93 |
| erniebot-4.0-202312 (文心一言) | 闭源 | 7.36 | 6.84 | 7.88 |
| moonshot-v1-32k-202404 (月之暗面) | 闭源 | 7.22 | 6.42 | 8.02 |
| Qwen1.5-72B-Chat (通义千问) | 开源 | 7.19 | 6.45 | 7.93 |
| DeepSeek-67B-Chat | 开源 | 6.43 | 5.75 | 7.11 |
| Yi-34B-Chat(零一万物) | 开源 | 6.12 | 4.86 | 7.38 |
| Yi-34B-Chat (零一万物) | 开源 | 6.12 | 4.86 | 7.38 |
| gpt-3.5-turbo-0613 | 闭源 | 6.08 | 5.35 | 6.71 |
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