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inference: false |
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# longchat-13b-16k Model Card |
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## Usage |
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Please use load_model from FastChat or LongChat repo to load the model (or chatting API from FastChat). There is a monkey patch needed to use the model. |
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Usage referece: |
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(LongChat) python3 eval.py --model-name-or-path lmsys/longchat-13b-16k --task topics |
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(FastChat) python3 -m fastchat.serve.cli --model-path lmsys/longchat-13b-16k |
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Under the hood, the monkey patch is added in: |
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https://github.com/lm-sys/FastChat/blob/da0641e567cf93756b0978ab5a6b092e96f06240/fastchat/model/model_adapter.py#L429 |
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## Model details |
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**Model type:** |
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longchat-13b-16k is an open-source chatbot trained by fine-tuning llama-13b on user-shared conversations collected from ShareGPT, using the condensing rotary embedding technique reported in the [blog](https://lmsys.org/blog/2023-06-29-longchat). |
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**Model date:** |
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longchat-13b-16k was trained on June 2023. |
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**Organizations developing the model:** |
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The LongChat developers: Dacheng Li*, Rulin Shao*, Anze Xie, Ying Sheng, Lianmin Zheng, Ion Stoica, Xuezhe Ma, and Hao Zhang |
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**Paper or resources for more information:** |
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https://github.com/DachengLi1/LongChat |
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**Where to send questions or comments about the model:** |
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https://github.com/DachengLi1/LongChat |
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## Intended use |
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**Primary intended uses:** |
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The primary use of longchat-13b-16k is for research purposes. |
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**Primary intended users:** |
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The primary intended users of the model are researchers in natural language processing, machine learning, and artificial intelligence. |
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## Training dataset |
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18K conversations collected from ShareGPT.com. |
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## Evaluation dataset |
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A preliminary evaluation of the model quality is conducted by our released [LongEval](https://github.com/DachengLi1/LongChat). |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_lmsys__longchat-13b-16k) |
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| Metric | Value | |
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| Avg. | 46.32 | |
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| ARC (25-shot) | 53.58 | |
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| HellaSwag (10-shot) | 77.67 | |
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| MMLU (5-shot) | 45.24 | |
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| TruthfulQA (0-shot) | 47.07 | |
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| Winogrande (5-shot) | 70.09 | |
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| GSM8K (5-shot) | 4.17 | |
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| DROP (3-shot) | 26.42 | |
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