Instructions to use SimpleJev/JevAny-Robotics-4B-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use SimpleJev/JevAny-Robotics-4B-LoRA with PEFT:
from peft import PeftModel from transformers import AutoModel base_model = AutoModel.from_pretrained("Qwen/Qwen3.5-4B") model = PeftModel.from_pretrained(base_model, "SimpleJev/JevAny-Robotics-4B-LoRA") - Notebooks
- Google Colab
- Kaggle
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Download eval/transfer.md from SimpleJev/JevAny-Robotics-4B-LoRA: direct link, hf CLI and curl.
- Browser
- Download file 2.19 kB
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https://huggingface.co/SimpleJev/JevAny-Robotics-4B-LoRA/resolve/main/eval/transfer.md
- Command line
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hf download hf://SimpleJev/JevAny-Robotics-4B-LoRA/eval/transfer.md
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curl -L -o transfer.md https://huggingface.co/SimpleJev/JevAny-Robotics-4B-LoRA/resolve/main/eval/transfer.md
2.19 kB
base rows 1264, new rows 1264, paired 1154
| slice | n | base acc | new acc | Δacc [95% CI] | base NLL | new NLL | ΔNLL [95% CI] | new-only/base-only correct |
|---|---|---|---|---|---|---|---|---|
| variant=clean | 1046 | 78.68 | 78.39 | -0.29 [-1.75, +1.05] | 0.587 | 0.664 | +0.077 [+0.048, +0.109] | 22/25 |
| task=buried_emotion | 20 | 75.00 | 70.00 | -5.00 [-15.00, +0.00] | 0.988 | 1.376 | +0.387 [-0.063, +0.973] | 0/1 |
| task=buried_paws | 20 | 70.00 | 70.00 | +0.00 [+0.00, +0.00] | 0.985 | 1.144 | +0.158 [-0.027, +0.405] | 0/0 |
| task=buried_qnli | 20 | 90.00 | 95.00 | +5.00 [+0.00, +15.00] | 0.256 | 0.203 | -0.053 [-0.140, +0.042] | 1/0 |
| task=buried_tweet_offensive | 20 | 75.00 | 80.00 | +5.00 [+0.00, +15.00] | 0.430 | 0.391 | -0.038 [-0.077, -0.004] | 1/0 |
| task=composition_held_and_or | 32 | 100.00 | 90.62 | -9.38 [-28.12, +0.00] | 0.125 | 0.154 | +0.029 [-0.026, +0.120] | 0/3 |
| task=composition_held_conditional | 32 | 68.75 | 68.75 | +0.00 [-18.75, +18.75] | 0.582 | 0.556 | -0.026 [-0.117, +0.049] | 3/3 |
| task=composition_held_or_not | 32 | 90.62 | 96.88 | +6.25 [+0.00, +15.62] | 0.370 | 0.314 | -0.056 [-0.133, +0.015] | 2/0 |
| task=contrastive_authorization | 40 | 100.00 | 100.00 | +0.00 [+0.00, +0.00] | 0.015 | 0.001 | -0.014 [-0.022, -0.007] | 0/0 |
| task=contrastive_deadline | 40 | 52.50 | 52.50 | +0.00 [+0.00, +0.00] | 1.059 | 1.827 | +0.769 [+0.450, +1.112] | 0/0 |
| task=emotion | 80 | 86.25 | 85.00 | -1.25 [-7.50, +3.75] | 0.330 | 0.297 | -0.033 [-0.090, +0.025] | 2/3 |
| task=mmlu | 80 | 77.50 | 75.00 | -2.50 [-6.25, +0.00] | 0.609 | 0.697 | +0.088 [+0.012, +0.176] | 0/2 |
| task=mmlu_pro | 200 | 58.50 | 56.50 | -2.00 [-5.00, +0.50] | 1.300 | 1.406 | +0.106 [+0.028, +0.197] | 2/6 |
| task=paws | 80 | 75.00 | 78.75 | +3.75 [+0.00, +8.75] | 0.712 | 0.758 | +0.047 [-0.028, +0.135] | 3/0 |
| task=qnli | 80 | 95.00 | 90.00 | -5.00 [-10.00, -1.25] | 0.209 | 0.267 | +0.058 [-0.004, +0.129] | 0/4 |
| task=sciq | 80 | 97.50 | 97.50 | +0.00 [+0.00, +0.00] | 0.048 | 0.043 | -0.004 [-0.014, +0.007] | 0/0 |
| task=tweet_offensive | 80 | 81.25 | 81.25 | +0.00 [-3.75, +3.75] | 0.354 | 0.353 | -0.001 [-0.018, +0.017] | 1/1 |