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misa-jsc
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Model Details
Model Provider
misa-jsc
Model Tree
google/gemma-4-31B-it
Input Modalities
Output Modalities
Supported Functionality
| Developer | MISA JSC |
| Architecture | Gemma4ForConditionalGeneration, dense, 60 layers, hidden size 5376 |
| Parameters | ~31B |
| Context length | 256K tokens |
| Vocabulary | 262K |
| Inputs | Text, images |
| Output | Probabilities over answer labels (the model generates no tokens) |
| Precision | bf16 safetensors, 2 shards, ~62.6 GB |
Each request has a state (the text to judge, plus optional images) and one or more questions. The model reads the state once in a single prefill pass. It then returns a probability distribution over each question's answer labels. No tokens are sampled, so results are deterministic and latency is close to prefill time.
Three question types are supported:
| Type | Answer |
|---|---|
noul (yes/no) | Probability that the statement is true |
choice | One label out of a set of named options, with per-option probabilities and a confidence |
score | An ordinal level from an ordered list (e.g. none / mild / severe), with per-level probabilities |
Each answer also returns a label mass (x_label_mass / label_mass). This is the share of the model's probability
that fell on the valid labels. A low label mass means the question did not fit the state well, so treat that answer as
low-confidence.
Recent SGLang releases serve /v1/systemone and /v1/decisions out of the box:
bash
python3 -m sglang.launch_server \ --model-path misa-jsc/misa-ai-2.0-decision-plus \ --default-chat-template-kwargs '{"enable_thinking": false}' \ --port 30000bash
curl -X POST http://localhost:30000/v1/systemone \ -H "Content-Type: application/json" \ -d '{ "model": "misa-jsc/misa-ai-2.0-decision-plus", "state": "Customer: I paid 3 days ago and still have not received my invoice. This is frustrating!", "questions": { "complaint": {"type": "noul", "instructions": "The customer is complaining"}, "intent": {"type": "choice", "instructions": "Main purpose of the message", "criteria": {"question": "Asking for information", "complaint": "Complaint", "order": "Placing an order"}} } }'To pass images, add an images array of base64 data URLs or {"url": ...} objects.
choice question over many yes/no questions when the answers are mutually exclusive (e.g. which tool to
call). It is faster and better calibrated.instructions short and concrete. Name the subject explicitly ("The customer …", "The image …").The weights are released under the Gemma 4 license (Apache 2.0), inherited from the base model.
python3 -m sglang.launch_server \ --model-path misa-jsc/misa-ai-2.0-decision-plus \ --default-chat-template-kwargs '{"enable_thinking": false}' \ --port 30000curl -X POST http://localhost:30000/v1/systemone \ -H "Content-Type: application/json" \ -d '{ "model": "misa-jsc/misa-ai-2.0-decision-plus", "state": "Customer: I paid 3 days ago and still have not received my invoice. This is frustrating!", "questions": { "complaint": {"type": "noul", "instructions": "The customer is complaining"}, "intent": {"type": "choice", "instructions": "Main purpose of the message", "criteria": {"question": "Asking for information", "complaint": "Complaint", "order": "Placing an order"}} } }'