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robometer-4b-fft-so101
aliangdw/robometer-4b-fft-so101
robometer-4b-fft-so101 is a machine learning model from aliangdw. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
Full fine-tune (FFT, no LoRA) of Robometer-4B on both SO-101 datasets: - Armnet benchmark so101 (villekuosmanenarmnetbenchrobometerv01so101) - MolmoACT2 so101 (ykorkmazmolmoact2so100101rbmmolmoact2so100101)
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At a glance
- Library
- transformers
- License
- apache-2.0
- Model type
- qwen3_vl
- Access
- Public
- Created
- Aug 15, 2026
- Updated
- Aug 15, 2026
- SHA
- 89f83820
Base models
Model card
From the Hugging Face model README
Robometer-4B FFT finetuned on both so101 datasets (Armnet so101 + MolmoACT2 so101)
Full fine-tune (FFT, no LoRA) of Robometer-4B on both SO-101 datasets:
- Armnet benchmark so101 (
villekuosmanen_armnetbench_robometer_v01_so101) - MolmoACT2 so101 (
ykorkmaz_molmoact2_so100_101_rbm_molmoact2_so100_101)
Qwen3-VL-4B backbone, 1500 steps on 4x H200. This is the best checkpoint (step 750).
Key result: training on both so101 datasets improves generalization
| Metric | armnet-only finetune | this (both so101) |
|---|---|---|
| Armnet so101 reward-alignment Pearson | 0.766 | 0.782 |
| Armnet so101 policy-ranking Kendall | 0.973 | 0.94 |
| Molmoact so101 reward-alignment Pearson (held-out) | 0.751 | 0.902 |
Training on both datasets raised molmoact so101 Pearson from 0.75 → 0.90 while keeping armnet performance roughly intact.
- Library
- transformers
- Type
- qwen3_vl
- License
- apache-2.0
- Created
- Aug 15, 2026
- Updated
- Aug 15, 2026