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Qwen2.5-7B-Anvita

Text Generation

text-generation-inference

Inference Endpoints

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sethuiyer commited on 25 days ago

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@@ -139,7 +139,7 @@ This combination optimizes Anvita for superior reasoning, dynamic conversations,
 | **MuSR (0-Shot)**        | 13.47        |
 | **MMLU-PRO (5-Shot)**    | 35.17        |
-Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/sethuiyer__Qwen2.5-7B-Anvita-details).
 Personal Benchmarks - check [PERSONAL_BENCHMARK.md](./PERSONAL_BENCHMARK.md)
 For optimal reasoning performance, it is recommended to use **BF16** precision and the [Entropic Chain of Thought](https://huggingface.co/sethuiyer/Qwen2.5-7B-Anvita/blob/main/entropic_cot.py) decoding method. This experimental decoder combines entropy and CoT decoding to enhance output quality.

 | **MuSR (0-Shot)**        | 13.47        |
 | **MMLU-PRO (5-Shot)**    | 35.17        |
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/results/blob/main/sethuiyer/Qwen2.5-7B-Anvita/results_2024-10-27T11-40-06.834908.json).
 Personal Benchmarks - check [PERSONAL_BENCHMARK.md](./PERSONAL_BENCHMARK.md)
 For optimal reasoning performance, it is recommended to use **BF16** precision and the [Entropic Chain of Thought](https://huggingface.co/sethuiyer/Qwen2.5-7B-Anvita/blob/main/entropic_cot.py) decoding method. This experimental decoder combines entropy and CoT decoding to enhance output quality.