Migel Tissera
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adding media folder
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README.md
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The actor network was trained with Supervised Fine-Tuning, on 250K very high-quality samples. It has 75K of Open-Orca's Chain-of-Thought data, and a mixture of Dolphin (GPT-4), SynthIA's Tree-of-Thought data.
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## Phase 2: Critic
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To train the critic, the following process was followed:
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regenerator_response = generate_text(prompt_regenerator, model_regenerator, tokenizer_regenerator)
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print(f"REGENERATION: {regenerator_response}")
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```
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The actor network was trained with Supervised Fine-Tuning, on 250K very high-quality samples. It has 75K of Open-Orca's Chain-of-Thought data, and a mixture of Dolphin (GPT-4), SynthIA's Tree-of-Thought data.
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Here are the results for the Actor network on metrics used by [HuggingFaceH4 Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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|:------:|:--------:|:-------:|
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|**Task**|**Metric**|**Value**|
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|*arc_challenge*|acc_norm|62.28|
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|*hellaswag*|acc_norm|83.22|
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|*mmlu*|acc_norm|63.10|
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|*truthfulqa_mc*|mc2|50.10|
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|**Total Average**|-|**0.64675**||
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## Phase 2: Critic
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To train the critic, the following process was followed:
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regenerator_response = generate_text(prompt_regenerator, model_regenerator, tokenizer_regenerator)
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print(f"REGENERATION: {regenerator_response}")
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```
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