HAL-X/ thx-02:q4_k_m

8,209 16 hours ago

Open 4.5B multimodal decision model by HAL-X: send a message, document or image with your options and get a calibrated probability for each one. #2 on Decision Index, 12K+ downloads HF, 20+ languages. Apache-2.0.

vision
ollama run HAL-X/thx-02:q4_k_m

Details

17 hours ago

4997f247259e · 3.7GB

qwen35
·
4.84B
·
Q4_K_M
clip
·
334M
·
BF16

Readme

THX-02 is an open multimodal decision model by HAL-X. You give it a state (a message, a ticket, a document, a JSON record, an agent trace or an image) and typed questions in plain language. It returns an answer to every question with a calibrated probability for every option. It does not generate text, so it can never answer outside your options.

  • #2 on the public Decision Index 0.3, ahead of 27B decision models, with 4.5B parameters
  • Multimodal: text and up to 4 images per request (charts, receipts, forms, screenshots, photos)
  • Multilingual: 20+ languages, including Arabic, Azerbaijani, Kazakh, Uzbek, Russian, Chinese and Japanese
  • No training for new tasks: your options are part of the request
  • Apache-2.0

Usage

ollama pull hal-x/thx-02
curl http://localhost:11434/v1/systemone -d '{
  "model": "hal-x/thx-02",
  "state": "AED 450 was charged twice at the same shop. Please refund the duplicate now.",
  "questions": {
    "team":   {"type": "choice", "instructions": "Which team should handle this?",
               "criteria": {"cards": "Card payments, disputes and refunds", "loans": "Loans and financing", "fraud": "Fraud and stolen cards"}},
    "refund": {"type": "noul", "instructions": "Does the customer ask for money back?"},
    "anger":  {"type": "score", "instructions": "How frustrated is the customer?", "criteria": ["calm", "annoyed", "angry"]}
  }
}'

Question types: choice (up to 255 options), noul (yes/no, returns the probability of yes) and score (an ordered scale of 2 to 10 levels). Images go in "images" as base64 strings.

Tags

Tag Size Notes
latest, q8_0 5.8 GB Default, same answers as the original model
q4_k_m 3.7 GB Laptops and small GPUs, 97.5% top-1 agreement with Q8_0
bf16 10 GB Reference precision

Notes

This build answers directly. The self-gated thinking of the original model (a short thought before hard questions) is available in the PyTorch release and the HAL-X API: 63.8 without thinking and 65.06 with it on the Decision Index 0.3 sample.