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
add README.md
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README.md
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The answer is the single output token; `top_logprobs` gives the probabilities (e.g. `Y 0.99`, `N 0.01`).
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### System One API
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```
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The answer is the single output token; `top_logprobs` gives the probabilities (e.g. `Y 0.99`, `N 0.01`).
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Context: up to 32,768 tokens. The System One server and client default to 1,024 tokens, so for longer inputs start the server with `SYSTEM1_MAX_CONTEXT_TOKENS=32768 SYSTEM1_MAX_PAYLOAD_CHARS=262144` (and vLLM with `--max-model-len 32768`, as above). The Decision Index run used exactly that setting: 110,352 of 110,520 requests fit, including contract documents of 6k+ tokens, and the 168 that did not fit count as wrong. The speed numbers above are for prompts up to 1,024 tokens; longer prompts take longer.
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### System One API
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