fhai50032/SamChat
SamChat is a 7 billion parameter language model developed by fhai50032, created by merging Intel/neural-chat-7b-v3-3 and cognitivecomputations/samantha-mistral-7b using the DARE TIES method. This model achieves an average score of 61.68 on the Open LLM Leaderboard, demonstrating capabilities across various reasoning and language understanding tasks. It is designed for general-purpose conversational AI and text generation, leveraging the strengths of its merged components.
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SamChat: A Merged 7B Language Model
SamChat is a 7 billion parameter language model developed by fhai50032, constructed through a merge of two distinct models: Intel/neural-chat-7b-v3-3 and cognitivecomputations/samantha-mistral-7b. The merging process utilized the DARE TIES method, with macadeliccc/WestLake-7B-v2-laser-truthy-dpo serving as the base model.
Key Capabilities & Performance
SamChat demonstrates solid performance across a range of benchmarks, as evaluated on the Open LLM Leaderboard. It achieves an average score of 61.68, with notable results in:
- AI2 Reasoning Challenge (25-Shot): 62.20
- HellaSwag (10-Shot): 81.88
- MMLU (5-Shot): 59.70
- Winogrande (5-Shot): 72.14
These scores indicate its proficiency in general reasoning, common sense understanding, and multi-task language comprehension. The model's configuration includes specific weight and density parameters for its merged components, optimized for float16 dtype.
Use Cases
SamChat is suitable for general text generation and conversational AI applications where a 7B parameter model with balanced reasoning and language understanding capabilities is required. Its merged architecture aims to combine the strengths of its constituent models for improved overall performance.