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--- |
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base_model: baichuan-inc/Baichuan2-7B-Chat |
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inference: false |
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library_name: transformers |
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license_name: baichuan2-community-license |
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license_link: https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat/blob/main/Community%20License%20for%20Baichuan2%20Model.pdf |
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model_creator: Baichuan Intelligent Technology |
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model_name: Baichuan2 7B Chat |
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model_type: baichuan |
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pipeline_tag: text-generation |
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quantized_by: Second State Inc. |
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language: |
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- en |
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- zh |
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--- |
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<!-- header start --> |
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<!-- 200823 --> |
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<div style="width: auto; margin-left: auto; margin-right: auto"> |
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<img src="https://github.com/second-state/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;"> |
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</div> |
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<hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> |
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<!-- header end --> |
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# Baichuan2-7B-Chat-GGUF |
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## Original Model |
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[baichuan-inc/Baichuan2-7B-Chat](https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat) |
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## Run with LlamaEdge |
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- LlamaEdge version: [v0.2.8](https://github.com/second-state/LlamaEdge/releases/tag/0.2.8) and above |
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- Prompt template |
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- Prompt type: `baichuan-2` |
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- Prompt string |
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```text |
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以下内容为人类用户与与一位智能助手的对话。 |
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用户:你好！ |
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助手: |
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``` |
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- Reverse prompt: `用户:` |
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- Context size: `4096` |
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- Run as LlamaEdge service |
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```bash |
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wasmedge --dir .:. --nn-preload default:GGML:AUTO:Baichuan2-7B-Chat-Q5_K_M.gguf llama-api-server.wasm -p baichuan-2 -r '用户:' |
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``` |
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- Run as LlamaEdge command app |
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```bash |
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wasmedge --dir .:. --nn-preload default:GGML:AUTO:Baichuan2-7B-Chat-Q5_K_M.gguf llama-chat.wasm -p baichuan-2 -r '用户:' |
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``` |
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## Quantized GGUF Models |
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| Name | Quant method | Bits | Size | Use case | |
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| ---- | ---- | ---- | ---- | ----- | |
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| [Baichuan2-7B-Chat-Q2_K.gguf](https://huggingface.co/second-state/Baichuan2-7B-Chat-GGUF/blob/main/Baichuan2-7B-Chat-Q2_K.gguf) | Q2_K | 2 | 3.27 GB| smallest, significant quality loss - not recommended for most purposes | |
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| [Baichuan2-7B-Chat-Q3_K_L.gguf](https://huggingface.co/second-state/Baichuan2-7B-Chat-GGUF/blob/main/Baichuan2-7B-Chat-Q3_K_L.gguf) | Q3_K_L | 3 | 4.08 GB| small, substantial quality loss | |
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| [Baichuan2-7B-Chat-Q3_K_M.gguf](https://huggingface.co/second-state/Baichuan2-7B-Chat-GGUF/blob/main/Baichuan2-7B-Chat-Q3_K_M.gguf) | Q3_K_M | 3 | 3.78 GB| very small, high quality loss | |
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| [Baichuan2-7B-Chat-Q3_K_S.gguf](https://huggingface.co/second-state/Baichuan2-7B-Chat-GGUF/blob/main/Baichuan2-7B-Chat-Q3_K_S.gguf) | Q3_K_S | 3 | 3.43 GB| very small, high quality loss | |
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| [Baichuan2-7B-Chat-Q4_0.gguf](https://huggingface.co/second-state/Baichuan2-7B-Chat-GGUF/blob/main/Baichuan2-7B-Chat-Q4_0.gguf) | Q4_0 | 4 | 4.36 GB| legacy; small, very high quality loss - prefer using Q3_K_M | |
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| [Baichuan2-7B-Chat-Q4_K_M.gguf](https://huggingface.co/second-state/Baichuan2-7B-Chat-GGUF/blob/main/Baichuan2-7B-Chat-Q4_K_M.gguf) | Q4_K_M | 4 | 4.61 GB| medium, balanced quality - recommended | |
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| [Baichuan2-7B-Chat-Q4_K_S.gguf](https://huggingface.co/second-state/Baichuan2-7B-Chat-GGUF/blob/main/Baichuan2-7B-Chat-Q4_K_S.gguf) | Q4_K_S | 4 | 4.39 GB| small, greater quality loss | |
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| [Baichuan2-7B-Chat-Q5_0.gguf](https://huggingface.co/second-state/Baichuan2-7B-Chat-GGUF/blob/main/Baichuan2-7B-Chat-Q5_0.gguf) | Q5_0 | 5 | 5.23 GB| legacy; medium, balanced quality - prefer using Q4_K_M | |
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| [Baichuan2-7B-Chat-Q5_K_M.gguf](https://huggingface.co/second-state/Baichuan2-7B-Chat-GGUF/blob/main/Baichuan2-7B-Chat-Q5_K_M.gguf) | Q5_K_M | 5 | 5.36 GB| large, very low quality loss - recommended | |
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| [Baichuan2-7B-Chat-Q5_K_S.gguf](https://huggingface.co/second-state/Baichuan2-7B-Chat-GGUF/blob/main/Baichuan2-7B-Chat-Q5_K_S.gguf) | Q5_K_S | 5 | 5.23 GB| large, low quality loss - recommended | |
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| [Baichuan2-7B-Chat-Q6_K.gguf](https://huggingface.co/second-state/Baichuan2-7B-Chat-GGUF/blob/main/Baichuan2-7B-Chat-Q6_K.gguf) | Q6_K | 6 | 6.16 GB| very large, extremely low quality loss | |
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| [Baichuan2-7B-Chat-Q8_0.gguf](https://huggingface.co/second-state/Baichuan2-7B-Chat-GGUF/blob/main/Baichuan2-7B-Chat-Q8_0.gguf) | Q8_0 | 8 | 7.98 GB| very large, extremely low quality loss - not recommended | |
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