Instructions to use logan7000/mllm-mmr1-gt-gemma3-12b-mmupt-full with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use logan7000/mllm-mmr1-gt-gemma3-12b-mmupt-full with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("logan7000/mllm-mmr1-gt-gemma3-12b-mmupt-full", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Gemma-3-12B - GT-Reward on MMR1 (mmupt recipe)
GRPO with ground-truth rewards, MMR1-Math-RL-Data-v0, 481 steps (mmupt recipe: beta=0.01, 10 generations/prompt, cap 2048, T=0.7, 12 prompts/update).
| folder | step | note |
|---|---|---|
best/ |
100 | best in-loop val (eval_reward = 0.375) |
endpoint/ |
481 | end of training |
Training note: five gradient spikes occurred (max grad-norm ~1.0e6, clipped at
1.0); best-by-val fell early (s100) and the in-loop curve was flat afterwards.
Step-count fingerprint: 481 = mmupt recipe (the older recipe runs 722 steps).
training/ holds metrics, train.log, and the Slurm joblog.
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