Image-Text-to-Text
PEFT
Safetensors
laboratory
protocol-aligned-action-prediction
lora
qwen
long-horizon-planning
conversational
Instructions to use Backup-SU-CongLab/LabHorizon-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Backup-SU-CongLab/LabHorizon-Model with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.6-35B-A3B") model = PeftModel.from_pretrained(base_model, "Backup-SU-CongLab/LabHorizon-Model") - Notebooks
- Google Colab
- Kaggle
Download all_results.json from Backup-SU-CongLab/LabHorizon-Model: direct link, hf CLI and curl.
- Browser
- Download file 350 Bytes
-
https://huggingface.co/Backup-SU-CongLab/LabHorizon-Model/resolve/main/all_results.json
- Command line
-
hf download hf://Backup-SU-CongLab/LabHorizon-Model/all_results.json
-
curl -L -o all_results.json https://huggingface.co/Backup-SU-CongLab/LabHorizon-Model/resolve/main/all_results.json
350 Bytes
| { | |
| "epoch": 10.0, | |
| "eval_loss": 0.44259119033813477, | |
| "eval_runtime": 27.1503, | |
| "eval_samples_per_second": 14.733, | |
| "eval_steps_per_second": 2.468, | |
| "total_flos": 3.634151342457697e+19, | |
| "train_loss": 0.2690703985452652, | |
| "train_runtime": 10014.7733, | |
| "train_samples_per_second": 5.991, | |
| "train_steps_per_second": 0.25 | |
| } |