Instructions to use Nhckdvrl/cvis-ablate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nhckdvrl/cvis-ablate with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Nhckdvrl/cvis-ablate", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Nhckdvrl/cvis-ablate: direct link, hf CLI and curl.
- Browser
- Download file 2.86 kB
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https://huggingface.co/Nhckdvrl/cvis-ablate/resolve/main/README.md
- Command line
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hf download hf://Nhckdvrl/cvis-ablate/README.md
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curl -L -o README.md https://huggingface.co/Nhckdvrl/cvis-ablate/resolve/main/README.md
2.86 kB
| tags: | |
| - c-vis | |
| - ablation | |
| - grpo | |
| - qwen3.5 | |
| library_name: transformers | |
| # C-VIS ablation checkpoints (cvis_ablate) | |
| Solver weights from the C-VIS ablation study (branch `cvis_ablate`). Each folder holds one finished round: a full Qwen3.5-9B fine-tune after 160 GRPO optimizer steps, trained with ms-swift. | |
| Layout: `<experiment>/round_<N>/` contains the safetensors shards, config, tokenizer, processor, chat template and `trainer_state.json` (training log history). Optimizer states are not included. | |
| | Experiment | Round | MathVision | MathVista | MMMU | Folder | | |
| | --- | --- | --- | --- | --- | --- | | |
| | a4_fixed_images | 1 | 50.66% | 81.80% | 68.67% | `a4_fixed_images/round_1` | | |
| | a4_fixed_images | 2 | 51.64% | 82.60% | 68.44% | `a4_fixed_images/round_2` | | |
| | a4_fixed_images | 3 | 53.95% | 82.80% | 68.22% | `a4_fixed_images/round_3` | | |
| | a4_fixed_images | 4 | 52.30% | 82.90% | 68.56% | `a4_fixed_images/round_4` | | |
| | c1_no_history | 1 | 53.29% | 82.60% | 69.22% | `c1_no_history/round_1` | | |
| | c1_no_history | 2 | 53.29% | 82.40% | 69.00% | `c1_no_history/round_2` | | |
| | c1_no_history | 3 | 54.28% | 83.20% | 70.11% | `c1_no_history/round_3` | | |
| | c1_no_history | 4 | 50.33% | 82.80% | 70.89% | `c1_no_history/round_4` | | |
| | c2_uniform_difficulty | 1 | 52.63% | 81.80% | 70.33% | `c2_uniform_difficulty/round_1` | | |
| | c2_uniform_difficulty | 2 | 54.61% | 83.20% | 71.44% | `c2_uniform_difficulty/round_2` | | |
| | c2_uniform_difficulty | 3 | 54.61% | 82.90% | 69.00% | `c2_uniform_difficulty/round_3` | | |
| | c2_uniform_difficulty | 4 | 51.97% | 82.50% | 69.78% | `c2_uniform_difficulty/round_4` | | |
| | c3_uniform_category | 1 | 53.29% | 83.70% | 70.11% | `c3_uniform_category/round_1` | | |
| | c3_uniform_category | 2 | 53.95% | 81.80% | 70.67% | `c3_uniform_category/round_2` | | |
| | c3_uniform_category | 3 | 53.95% | 82.80% | 69.78% | `c3_uniform_category/round_3` | | |
| | c3_uniform_category | 4 | 54.61% | 83.40% | 71.44% | `c3_uniform_category/round_4` | | |
| | c4_no_difficulty_filter | 1 | 54.93% | 82.00% | 69.78% | `c4_no_difficulty_filter/round_1` | | |
| | c4_no_difficulty_filter | 2 | 56.25% | 82.10% | 69.00% | `c4_no_difficulty_filter/round_2` | | |
| | c4_no_difficulty_filter | 3 | 55.59% | 83.20% | 70.00% | `c4_no_difficulty_filter/round_3` | | |
| | c4_no_difficulty_filter | 4 | 57.57% | 82.30% | 70.11% | `c4_no_difficulty_filter/round_4` | | |
| Experiments: | |
| - `c1_no_history`: curriculum without history. | |
| - `c2_uniform_difficulty`: uniform difficulty curriculum. | |
| - `c3_uniform_category`: uniform category curriculum. | |
| - `c4_no_difficulty_filter`: no difficulty filter. | |
| - `a4_fixed_images`: fixed image pool. | |
| Scores are the full benchmark splits used by the C-VIS evaluation (MathVision 304, MathVista 1000, MMMU 900), sampled at temperature 0.7. More rounds are added as runs finish. | |
| Load one round with `from_pretrained("<repo>", subfolder="c4_no_difficulty_filter/round_4")`. | |