Feature Extraction
Transformers
Safetensors
vtx_jev
system-one
jev
decision-model
typed-decisions
4-bit precision
lf4
calibrated-probabilities
noul
choice
score
custom_code
8-bit precision
Instructions to use VTXAI/VTX-JEV-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VTXAI/VTX-JEV-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="VTXAI/VTX-JEV-1", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("VTXAI/VTX-JEV-1", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download evaluation.json from VTXAI/VTX-JEV-1: direct link, hf CLI and curl.
- Browser
- Download file 1.59 kB
-
https://huggingface.co/VTXAI/VTX-JEV-1/resolve/main/evaluation.json
- Command line
-
hf download hf://VTXAI/VTX-JEV-1/evaluation.json
-
curl -L -o evaluation.json https://huggingface.co/VTXAI/VTX-JEV-1/resolve/main/evaluation.json
1.59 kB
| { | |
| "model": "VTXAI/VTX-JEV-1", | |
| "dataset": "SargeDev/jev-distill-corpus-v3", | |
| "training_rows": 655806, | |
| "training_epochs": 2, | |
| "fp32_source_sha256": "0afb042d973cc20182acf6080e061520887b706dfd1e96eb8f82e5502eae9352", | |
| "lf4_model_sha256": "bfbc52552a7a820aa1d77b922dfa52baa3d2d87f040d5df6d5e46bada337feaf", | |
| "model_bytes": 8002584, | |
| "fp32_model_bytes": 50654168, | |
| "storage_compression": 6.329726498341036, | |
| "held_out_3000": { | |
| "fp32": { | |
| "accuracy": 0.7263333333333334, | |
| "nll": 0.8874788624610132, | |
| "brier": 0.07555933721830775, | |
| "ece": 0.13562145072718462, | |
| "score_mae": 0.2316402201820165, | |
| "score_rps": 0.008217289567585908, | |
| "cpu_eval_seconds": 120.24935622200064 | |
| }, | |
| "lf4": { | |
| "accuracy": 0.731, | |
| "nll": 0.8882363581924388, | |
| "brier": 0.07580398834944735, | |
| "ece": 0.13725309281547865, | |
| "score_mae": 0.23082155831344425, | |
| "score_rps": 0.008179817874272658, | |
| "cpu_eval_seconds": 97.024852681001 | |
| } | |
| }, | |
| "held_out_1000_t4": { | |
| "hardware": "Tesla T4", | |
| "fp32": { | |
| "accuracy": 0.733, | |
| "nll": 0.8896643671365455, | |
| "brier": 0.07663099930399228, | |
| "ece": 0.13973448510468006, | |
| "score_mae": 0.21455059151449604, | |
| "score_rps": 0.007221793114608037, | |
| "eval_seconds": 1.8453442480000035 | |
| }, | |
| "lf4": { | |
| "accuracy": 0.73, | |
| "nll": 0.8905277721015736, | |
| "brier": 0.07685185967836446, | |
| "ece": 0.1336674015074968, | |
| "score_mae": 0.21391292604711294, | |
| "score_rps": 0.007138724646240176, | |
| "eval_seconds": 0.6449195789999749 | |
| } | |
| } | |
| } | |