Text Classification
Transformers
Safetensors
qwen3_5
image-text-to-text
decision-model
typed-decisions
classification
routing
calibrated-probabilities
nvfp4
4-bit precision
8-bit precision
compressed-tensors
Instructions to use ironbcc/Seb-9B-v2-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ironbcc/Seb-9B-v2-NVFP4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ironbcc/Seb-9B-v2-NVFP4")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("ironbcc/Seb-9B-v2-NVFP4") model = AutoModelForMultimodalLM.from_pretrained("ironbcc/Seb-9B-v2-NVFP4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download generation_config.json from ironbcc/Seb-9B-v2-NVFP4: direct link, hf CLI and curl.
- Browser
- Download file 116 Bytes
-
https://huggingface.co/ironbcc/Seb-9B-v2-NVFP4/resolve/main/generation_config.json
- Command line
-
hf download hf://ironbcc/Seb-9B-v2-NVFP4/generation_config.json
-
curl -L -o generation_config.json https://huggingface.co/ironbcc/Seb-9B-v2-NVFP4/resolve/main/generation_config.json
116 Bytes
| { | |
| "_from_model_config": true, | |
| "eos_token_id": 248044, | |
| "transformers_version": "5.17.0", | |
| "use_cache": true | |
| } | |