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
Download training_metrics.json from SimpleJev/JevAny-Robotics-4B-LoRA: direct link, hf CLI and curl.
- Browser
- Download file 645 Bytes
-
https://huggingface.co/SimpleJev/JevAny-Robotics-4B-LoRA/resolve/main/training_metrics.json
- Command line
-
hf download hf://SimpleJev/JevAny-Robotics-4B-LoRA/training_metrics.json
-
curl -L -o training_metrics.json https://huggingface.co/SimpleJev/JevAny-Robotics-4B-LoRA/resolve/main/training_metrics.json
645 Bytes
| { | |
| "wall_seconds": 103920.80980944633, | |
| "records_seen": 184942, | |
| "requested_records": 184942, | |
| "distributed_padding_records": 0, | |
| "truncated_records": 0, | |
| "rejected_records": 0, | |
| "optimizer_steps": 5780, | |
| "planned_optimizer_steps": 5780, | |
| "forward_tokens": 261189796, | |
| "early_stopped": false, | |
| "early_stop_metric": "development_acc", | |
| "early_stop_best": null, | |
| "early_stop_best_step": null, | |
| "selected_checkpoint": null, | |
| "early_stop_stale_evaluations": 0, | |
| "peak_device_bytes": 25768000512, | |
| "device": "cuda", | |
| "dtype": "bf16", | |
| "batch": 1, | |
| "world_size": 2, | |
| "global_effective_batch": 32, | |
| "peak_rss_bytes": 9781452800 | |
| } | |