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README.md
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<div align="center">
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π Paper: https://arxiv.org/abs/2402.15506
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If you already know [Mixtral](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1), xLAM-v0.1 is a significant upgrade and better at many things. For the same number of parameters, the model have been fine-tuned across a wide range of agent tasks and scenarios, all while preserving the capabilities of the original model.
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<tr><td>Vicuna-13B-16K </td><td>0.033</td><td>0.343</td></tr>
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<tr><td>Llama-2-70B </td><td>0.000</td><td>0.483</td></tr>
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license: cc-by-nc-4.0
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<div align="center">
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π Paper: https://arxiv.org/abs/2402.15506
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License: cc-by-nc-4.0
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If you already know [Mixtral](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1), xLAM-v0.1 is a significant upgrade and better at many things. For the same number of parameters, the model have been fine-tuned across a wide range of agent tasks and scenarios, all while preserving the capabilities of the original model.
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<tr><td>Vicuna-13B-16K </td><td>0.033</td><td>0.343</td></tr>
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<tr><td>Llama-2-70B </td><td>0.000</td><td>0.483</td></tr>
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</tbody>
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</table>
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