mayacinka/yam-sam-7B
yam-sam-7B is a 7 billion parameter language model created by mayacinka, formed by merging cognitivecomputations/samantha-mistral-7b, CorticalStack/shadow-clown-7B-dare, and yam-peleg/Experiment26-7B using the dare_ties merge method. This model achieves an average score of 74.58 on the Open LLM Leaderboard, demonstrating strong performance across various reasoning and language understanding benchmarks. With a context length of 4096 tokens, it is suitable for general-purpose text generation and understanding tasks.
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Overview
yam-sam-7B is a 7 billion parameter language model developed by mayacinka. It is a product of merging three distinct models: cognitivecomputations/samantha-mistral-7b, CorticalStack/shadow-clown-7B-dare, and yam-peleg/Experiment26-7B. The merge was performed using the dare_ties method, with yam-peleg/Experiment27-7B serving as the base model.
Key Capabilities & Performance
This model demonstrates solid performance across a range of benchmarks, as evaluated on the Open LLM Leaderboard. It achieves an average score of 74.58.
Key benchmark results include:
- AI2 Reasoning Challenge (25-Shot): 70.90
- HellaSwag (10-Shot): 87.92
- MMLU (5-Shot): 65.39
- TruthfulQA (0-shot): 71.30
- Winogrande (5-shot): 83.03
- GSM8k (5-shot): 68.92
Usage
The model supports standard text generation tasks and can be easily integrated using the Hugging Face transformers library. It is configured for bfloat16 dtype and includes int8_mask parameters in its merge configuration.
Good For
- General-purpose text generation and understanding.
- Applications requiring a balance of reasoning, common sense, and language comprehension.
- Developers looking for a merged model with competitive benchmark scores in the 7B parameter class.