jan-hq/trinity-v1
jan-hq/trinity-v1 is a 7 billion parameter language model created by Jan, developed using the Slerp merge method. It combines viethq188/LeoScorpius-7B-Chat-DPO and GreenNode/GreenNodeLM-7B-v1olet, with GreenNode/GreenNodeLM-7B-v1olet as its base model. This merged model is designed for general conversational AI tasks, demonstrating competitive performance across various benchmarks including an average score of 74.8 on the OpenLLM Leaderboard.
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Model Overview
jan-hq/trinity-v1 is a 7 billion parameter language model developed by Jan, utilizing the Slerp merge method. It integrates two high-performing models from the OpenLLM Leaderboard as of December 14th: viethq188/LeoScorpius-7B-Chat-DPO and GreenNode/GreenNodeLM-7B-v1olet, with the latter serving as its base model. This model is a test project for merging techniques, aiming to combine the strengths of its constituent models.
Key Capabilities & Performance
Trinity-v1 demonstrates solid performance across a range of benchmarks, as evaluated on the OpenLLM Leaderboard. Its average score is 74.8, with specific results including:
- ARC (25-shot): 72.27
- HellaSwag (10-shot): 88.36
- MMLU (5-shot): 65.2
- TruthfulQA (0-shot): 69.31
- Winogrande (5-shot): 82
- GSM8K (5-shot): 71.65
Usage and Development
This model is optimized for use with a specific prompt template:
{system_message}
### Instruction:
{prompt}
### Response:It can be run locally using Jan Desktop, an open-source, offline-first ChatGPT alternative that offers an OpenAI-compatible local server. The development of this model leverages tools like mergekit and draws inspiration from techniques like DARE and SLERP for model merging.