abideen/AlphaMonarch-laser
AlphaMonarch-laser is a 7 billion parameter language model developed by abideen, fine-tuned using DPO from mlabonne/NeuralMonarch-7B. It leverages LaserQLoRA for improved performance over previous AlphaMonarch versions, specifically optimized on half of the projections. This model currently ranks first on the Yet Another LLM Leaderboard (YALL), demonstrating strong general performance across various benchmarks.
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AlphaMonarch-laser: A Performance-Optimized 7B Model
AlphaMonarch-laser is a 7 billion parameter language model developed by abideen, built upon the mlabonne/NeuralMonarch-7B base model. It distinguishes itself through a DPO (Direct Preference Optimization) fine-tuning approach, utilizing the argilla/OpenHermes2.5-dpo-binarized-alpha preference dataset. A key innovation is the application of LaserQLoRA, which has enabled this model to achieve superior performance compared to mlabonne/AlphaMonarch-7B, despite being fine-tuned on only half of the projections.
Key Capabilities & Performance
- Leaderboard Topper: AlphaMonarch-laser holds the #1 rank on the Yet Another LLM Leaderboard (YALL), indicating strong overall capabilities.
- Robust Benchmarking: Evaluation results from Nous Benchmark and OpenLLM Benchmark show competitive performance across various tasks, including:
- AGIEVAL: Average 28.41%
- GPT4ALL: Average 76.98% (e.g., ARC-Challenge 66.30%, HellaSwag 69.60%)
- TruthfulQA: Average 70.71% (mc1 63.04%, mc2 78.39%)
- BIGBENCH: Average 55.37%
- OpenLLM Benchmark: Average 73.5% (e.g., GSM8K 66.77%, Winogrande 84.6%)
Training Details
The model was trained for 1080 steps using specific hyperparameters, including a learning rate of 5e-07, a batch size of 1 (with 8 gradient accumulation steps), and an Adam optimizer. The fine-tuning process involved targeting specific q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, and down_proj modules within the model's layers using QLoRA.