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license: apache-2.0 |
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datasets: |
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- abacusai/MetaMathFewshot |
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- shahules786/orca-chat |
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- anon8231489123/ShareGPT_Vicuna_unfiltered |
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c14f6b02e1f8f67c73bd05/pf4d6FA7DriRtVq5HCkxd.png) |
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Trained on the MetamathFewshot (https://huggingface.co/datasets/abacusai/MetaMathFewshot) dataset from base Mistral, as well as the Vicuna (https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered) dataset and the OrcaChat (https://huggingface.co/datasets/shahules786/orca-chat) dataset. |
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Instruction tuned with the following parameters: |
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- LORA, Rank 8, Alpha 16, Dropout 0.05, all modules (QKV and MLP) |
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- 3 epochs |
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- Micro Batch Size 32 over 4xH100, gradient accumulation steps = 1 |
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- AdamW with learning rate 5e-5 |
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# Evaluation Results |
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### HuggingFace Leaderboard |
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| Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K | |
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| --- | --- | --- | --- | --- | --- | --- | |
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| 67.33 | 59.64 | 81.82 | 61.69 | 53.23 | 78.45 | 69.14 | |
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For comparison the GSM8K score for the original `metamath/MetaMath-Mistral-7B` was 68.84 and average score was 65.78. |
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### MT-Bench |
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First Turn: 6.9 |
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Second Turn: 6.51875 |
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**Average: 6.709375** |