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 tokenizer.json from ironbcc/Seb-9B-v2-NVFP4: direct link, hf CLI and curl.
- Browser
- Download file 20 MB
-
https://huggingface.co/ironbcc/Seb-9B-v2-NVFP4/resolve/main/tokenizer.json
- Command line
-
hf download hf://ironbcc/Seb-9B-v2-NVFP4/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/ironbcc/Seb-9B-v2-NVFP4/resolve/main/tokenizer.json
20 MB
- Xet hash:
- 777bcaa63794fa47b8f53680be9d6d176f1fcbd7ba03cdc6c3bae2b3d76b323f
- Size of remote file:
- 20 MB
- SHA256:
- 06b9509352d2af50381ab2247e083b80d32d5c0aba91c272ca9ff729b6a0e523
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