Text Classification
Transformers
Safetensors
English
qwen3_5_moe
image-text-to-text
system-one
typed-decisions
decision-model
calibrated-probabilities
jev
noul
choice
score
lora
mixture-of-experts
vllm
nvfp4
modelopt
fp4
Eval Results (legacy)
8-bit precision
Instructions to use autotrust/JEV-35B-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autotrust/JEV-35B-NVFP4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="autotrust/JEV-35B-NVFP4")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("autotrust/JEV-35B-NVFP4") model = AutoModelForMultimodalLM.from_pretrained("autotrust/JEV-35B-NVFP4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from autotrust/JEV-35B-NVFP4: direct link, hf CLI and curl.
- Browser
- Download file 390 Bytes
-
https://huggingface.co/autotrust/JEV-35B-NVFP4/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://autotrust/JEV-35B-NVFP4/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/autotrust/JEV-35B-NVFP4/resolve/main/preprocessor_config.json
390 Bytes
| { | |
| "size": { | |
| "longest_edge": 16777216, | |
| "shortest_edge": 65536 | |
| }, | |
| "patch_size": 16, | |
| "temporal_patch_size": 2, | |
| "merge_size": 2, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "processor_class": "Qwen3VLProcessor", | |
| "image_processor_type": "Qwen2VLImageProcessorFast" | |
| } |