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--- |
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license: apache-2.0 |
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library_name: transformers |
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datasets: |
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- argilla/distilabel-intel-orca-dpo-pairs |
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pipeline_tag: text-generation |
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model-index: |
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- name: Evangelion-7B |
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results: |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: AI2 Reasoning Challenge (25-Shot) |
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type: ai2_arc |
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config: ARC-Challenge |
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split: test |
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args: |
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num_few_shot: 25 |
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metrics: |
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- type: acc_norm |
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value: 68.94 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=VitalContribution/Evangelion-7B |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: HellaSwag (10-Shot) |
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type: hellaswag |
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split: validation |
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args: |
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num_few_shot: 10 |
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metrics: |
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- type: acc_norm |
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value: 86.45 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=VitalContribution/Evangelion-7B |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MMLU (5-Shot) |
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type: cais/mmlu |
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config: all |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 63.97 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=VitalContribution/Evangelion-7B |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: TruthfulQA (0-shot) |
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type: truthful_qa |
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config: multiple_choice |
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split: validation |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: mc2 |
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value: 64.01 |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=VitalContribution/Evangelion-7B |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: Winogrande (5-shot) |
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type: winogrande |
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config: winogrande_xl |
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split: validation |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 79.95 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=VitalContribution/Evangelion-7B |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: GSM8k (5-shot) |
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type: gsm8k |
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config: main |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 66.94 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=VitalContribution/Evangelion-7B |
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name: Open LLM Leaderboard |
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--- |
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<h1 align="center">🏠 Socials</h1> |
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<p align="center"> |
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🤗 <a href="https://huggingface.co/VitalContribution" target="_blank">HF Repo</a> • 🐦 <a href="https://twitter.com/VContribution" target="_blank">Twitter</a> |
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</p> |
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# Evangelion-7B |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/63ae02ff20176b2d21669dd6/-si1T5gSSjvg1QlfeFKDf.jpeg" width="500" height="600"> |
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|
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I was just curious to see if something special might happen if one uses: |
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$$ |
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\text{{high-quality DPO dataset}} + \text{{merge of DPO optimized and non-DPO optimized model}} |
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$$ |
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The underlying model that I used was `/Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp`. |
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# Dataset |
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Dataset: `/argilla/distilabel-intel-orca-dpo-pairs` |
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The dataset was roughly ~3000 samples but they were high quality (according to the chosen_score). |
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The following filters were applied to the original dataset: |
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```python |
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dataset = dataset.filter( |
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lambda r: |
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r["status"] != "tie" and |
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r["chosen_score"] >= 8 and |
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not r["in_gsm8k_train"] |
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) |
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``` |
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# Chat Template |
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I decided to go with the ChatML which is used for OpenHermes2.5 |
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By the way I integreated the chat template into the models tokenizer. |
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``` |
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<|im_start|>system |
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{system}<|im_end|> |
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<|im_start|>user |
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{user}<|im_end|> |
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<|im_start|>assistant |
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{asistant}<|im_end|> |
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``` |
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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_VitalContribution__Evangelion-7B) |
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| Metric |Value| |
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|---------------------------------|----:| |
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|Avg. |71.71| |
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|AI2 Reasoning Challenge (25-Shot)|68.94| |
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|HellaSwag (10-Shot) |86.45| |
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|MMLU (5-Shot) |63.97| |
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|TruthfulQA (0-shot) |64.01| |
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|Winogrande (5-shot) |79.95| |
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|GSM8k (5-shot) |66.94| |
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