Model Card for DeepAutoAI/ldm_soup_Llama-3.1-8B-Inst
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in
- compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
Overview
DeepAutoAI/ldm_soup_Llama-3.1-8B-Inst is developed by deepAuto.ai and builds upon the VAGOsolutions/Llama-3.1-SauerkrautLM-8B-Instruct model. Our approach leverages the base model’s pretrained weights and optimizes them for the Winogrande and ARC-Challenge datasets by training a latent diffusion model on the pretrained weights.
Through this process, we learn the distribution of the base model's weight space, enabling us to explore optimal configurations. We then sample multiple sets of weights, using the model-soup averaging technique to identify the best-performing weights for both datasets. These weights are merged using linear interpolation to create the final model weights for DeepAutoAI/ldm_soup_Llama-3.1-8B-Inst.
This approach has led to improved performance on previously unseen leaderboard tasks, all without any additional task-specific training.
The work is currently in progress
References
Diffusion-Based Neural Network Weights Generation
Evaluation
Results
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 28.64 |
| IFEval (0-Shot) | 80.33 |
| BBH (3-Shot) | 31.10 |
| MATH Lvl 5 (4-Shot) | 11.56 |
| GPQA (0-shot) | 5.26 |
| MuSR (0-shot) | 11.52 |
| MMLU-PRO (5-shot) | 32.07 |