maicomputer/gpt4-x-alpaca
maicomputer/gpt4-x-alpaca is a 13 billion parameter language model, fine-tuned from the Alpaca-13B base model. It was trained for three epochs on responses generated by GPT-4, aiming to imbue it with GPT-4's conversational style and reasoning capabilities. This model is designed for general-purpose instruction following, leveraging its fine-tuning to perform across various natural language understanding and generation tasks.
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maicomputer/gpt4-x-alpaca Overview
This model is a 13 billion parameter language model, built upon the Alpaca-13B base architecture. Its core differentiator lies in its training methodology: it has been extensively fine-tuned for three epochs using responses generated by GPT-4. This process aims to transfer the advanced conversational and reasoning patterns observed in GPT-4 to a more accessible 13B parameter model.
Key Capabilities & Performance
The model demonstrates general instruction-following capabilities, influenced by its GPT-4-derived training data. Its performance on the Open LLM Leaderboard provides insights into its strengths across various benchmarks:
- Average Score: 46.78
- ARC (25-shot): 52.82
- HellaSwag (10-shot): 79.59
- MMLU (5-shot): 48.19
- TruthfulQA (0-shot): 48.88
- Winogrande (5-shot): 70.17
- GSM8K (5-shot): 2.81 (indicating challenges with complex mathematical reasoning)
- DROP (3-shot): 24.99
These scores suggest a solid performance in common sense reasoning (HellaSwag, Winogrande) and general knowledge (MMLU), while highlighting areas for improvement in advanced reasoning tasks like GSM8K and DROP. The model's context length is 4096 tokens.
Good For
- General instruction following: Leveraging the GPT-4 fine-tuning for diverse prompts.
- Conversational AI: Benefiting from the style and coherence learned from GPT-4 responses.
- Experimentation: A good candidate for developers looking to explore models fine-tuned with advanced teacher models.