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 merge_record.json from ironbcc/Seb-9B-v2: direct link, hf CLI and curl.
- Browser
- Download file 235 Bytes
-
https://huggingface.co/ironbcc/Seb-9B-v2/resolve/main/merge_record.json
- Command line
-
hf download hf://ironbcc/Seb-9B-v2/merge_record.json
-
curl -L -o merge_record.json https://huggingface.co/ironbcc/Seb-9B-v2/resolve/main/merge_record.json
235 Bytes
| { | |
| "base": "/home/ironbcc/.cache/huggingface/hub/models--Qwen--Qwen3.5-9B/snapshots/c202236235762e1c871ad0ccb60c8ee5ba337b9a", | |
| "adapter": "/media/ironbcc/CrucialX10/runs/s1_di_m9/adapters", | |
| "scale": 20.0, | |
| "modules_merged": 248 | |
| } |