Robotics
PEFT
Safetensors
jevany
lora
decision-model
pointer
reward-model
judge
calibration
conformal-prediction
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
Update README.md
Browse files
README.md
CHANGED
|
@@ -33,7 +33,7 @@ failure localisation, trajectory preference), built only from train-side splits
|
|
| 33 |
|
| 34 |
| Model | Size | RoboPulse acc β | RoboPulse AUROC β | RoboPulse++ AUROC β | RoboPulse++ bal. acc β | RoboRewardBench MAE β | JevJudge-Robotics skill_role β |
|
| 35 |
|---|---:|---:|---:|---:|---:|---:|---:|
|
| 36 |
-
| **JevAny-4B-
|
| 37 |
| JevAny-Qwen3.5-4B-LoRA (parent) | 4B | 52.7 | 0.588 | 0.641 | 59.1 | 1.057 | 0.149 |
|
| 38 |
| Qwen3.5-4B zero-shot (constrained choice) | 4B | 49.9 | 0.519 | 0.592 | 50.0 | 1.261 | 0.203 |
|
| 39 |
| OneJev-4B | 4B | 53.9 | 0.586 | 0.617 | 58.3 | 1.005 | 0.425 |
|
|
|
|
| 33 |
|
| 34 |
| Model | Size | RoboPulse acc β | RoboPulse AUROC β | RoboPulse++ AUROC β | RoboPulse++ bal. acc β | RoboRewardBench MAE β | JevJudge-Robotics skill_role β |
|
| 35 |
|---|---:|---:|---:|---:|---:|---:|---:|
|
| 36 |
+
| **JevAny-Robotics-4B-LoRA (this model)** | 4B | **71.1** | **0.806** | **0.728** | **65.9** | **0.443** | 0.567 |
|
| 37 |
| JevAny-Qwen3.5-4B-LoRA (parent) | 4B | 52.7 | 0.588 | 0.641 | 59.1 | 1.057 | 0.149 |
|
| 38 |
| Qwen3.5-4B zero-shot (constrained choice) | 4B | 49.9 | 0.519 | 0.592 | 50.0 | 1.261 | 0.203 |
|
| 39 |
| OneJev-4B | 4B | 53.9 | 0.586 | 0.617 | 58.3 | 1.005 | 0.425 |
|