Text Classification
Transformers
Safetensors
English
qwen3
text-generation
qwen
decision-model
finance
wealth-management
sec-filings
news
calibration
lora
jev
text-embeddings-inference
Instructions to use ternary/Arbiter-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ternary/Arbiter-8B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ternary/Arbiter-8B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ternary/Arbiter-8B") model = AutoModelForCausalLM.from_pretrained("ternary/Arbiter-8B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
15 days ago
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Restore model card metadata
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Update README.md
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Rename to Arbiter-8B; state the Qwen3-8B base clearly
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Model card: requirements instead of test-machine details
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Model card: standard model details, how it works, versions
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Update README.md
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Arbiter v0.4 full model: LoRA merged into the dequantized 4-bit base (fp16), tokenizer, decision head, calibration; adapter kept in adapter/
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Arbiter v0.4: LoRA adapter, decision head, calibration, inference code and model card
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initial commit
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