Text Classification
Transformers
Safetensors
qwen3_5
image-text-to-text
decision-model
typed-decisions
classification
routing
calibrated-probabilities
Instructions to use ironbcc/Seb-9B-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ironbcc/Seb-9B-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ironbcc/Seb-9B-v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("ironbcc/Seb-9B-v2") model = AutoModelForMultimodalLM.from_pretrained("ironbcc/Seb-9B-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from ironbcc/Seb-9B-v2: direct link, hf CLI and curl.
- Browser
- Download file 12.8 MB
-
https://huggingface.co/ironbcc/Seb-9B-v2/resolve/main/tokenizer.json
- Command line
-
hf download hf://ironbcc/Seb-9B-v2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/ironbcc/Seb-9B-v2/resolve/main/tokenizer.json
12.8 MB
- Xet hash:
- 655d3f6803c619aa34c31a027ce74a315eaa200aad3fca5b968dcdf6485bfba2
- Size of remote file:
- 12.8 MB
- SHA256:
- 5f9e4d4901a92b997e463c1f46055088b6cca5ca61a6522d1b9f64c4bb81cb42
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