Tev1 requires Ollama 0.35 or later.

Tev1 is a family of experimental decision models from Together AI, fine-tuned from Qwen3.5 in 4B and 0.8B sizes.

You give it a question and a set of options. It selects the answer based on likely outcome, and gives you the probability of each option.

It’s meant for the fast classification work that TypeSafe’s Jev does, like routing a support ticket or checking a request against a policy.

Models

4B

ollama pull tev1:4b

0.8B

ollama pull tev1:0.8b

Highlights

• Three question types: Pick from a list, answer true or false, or place something on a rubric.
• Two sizes: The 4B model is the more accurate of the two. The 0.8B model is an 812MB download for tight memory budgets.
• Many questions per request: Ask up to 64 questions about the same text in one call.
• Open: The dataset builders and training scripts are MIT licensed.

API

Decision models use Ollama’s /v1/systemone endpoint, which follows TypeSafe’s Jev API. Put the text you want judged in state and your questions in questions. Ollama builds Tev1’s prompt for you.

Every question has a type, instructions, and usually criteria:

Choice and score questions take 2 to 26 options. Tev1 was trained on 2 to 24, so stay in that range. confidence runs from 0 to 1 and shows how concentrated the probabilities are. It isn’t the chance that the answer is right.

Examples

cURL

curl http://localhost:11434/v1/systemone -d '{
  "model": "tev1",
  "state": "Customer message: Hi, I checked my statement and your company charged my card twice for the October subscription. The amounts are both $19.99 on the same day. I have not changed my plan.",
  "questions": {
    "intent": {
      "type": "choice",
      "instructions": "Which listed support intent best matches the customer message?",
      "criteria": {
        "duplicate_charge": "The customer reports being charged more than once.",
        "cancel_subscription": "The customer wants to end or downgrade a subscription.",
        "card_declined": "The customer reports a payment that failed or was declined.",
        "none": "None of the listed intents matches."
      }
    },
    "refund": {
      "type": "noul",
      "instructions": "Does the customer explicitly ask for a refund?"
    }
  }
}'

The response has an entry under answers for each question. intent comes back with the chosen choice, the probabilities of all four options, and a confidence. refund comes back as noul, the probability that the answer is true.

To use the smaller model, set "model": "tev1:0.8b".

Python

Use TypeSafe’s official Python SDK and point it at Ollama. The SDK requires an API key, but Ollama ignores it, so any value works.

pip install typesafe-sdk
export TYPESAFE_BASE_URL=http://localhost:11434
export TYPESAFE_API_KEY=ollama
export TYPESAFE_DEFAULT_MODEL=tev1

from typesafe_sdk import Choice, Noul, Score, TypeSafeClient

state = {
    "policy": "Returns are allowed within 30 days of purchase.",
    "request": "I bought these headphones 12 days ago and want to send them back.",
}
questions = {
    "eligible": Noul(
        instructions="Is this return within the allowed window?",
    ),
    "category": Choice(
        instructions="What kind of request is this?",
        criteria={
            "return": "The customer wants to send an item back.",
            "exchange": "The customer wants a different item instead.",
            "other": "None of the above.",
        },
    ),
    "sentiment": Score(
        instructions="How positive is the customer's message?",
        criteria=["Negative", "Neutral", "Positive"],
    ),
}

with TypeSafeClient(timeout=120) as client:
    result = client.system_one(state=state, questions=questions)

print(result.nouls["eligible"].noul)          # probability the return is allowed
print(result.choices["category"].choice)      # the chosen category
print(result.scores["sentiment"].score)       # expected level, from 0 to 2

Decision models aren’t in the Ollama CLI or the Ollama Python and JavaScript libraries yet. Use the API or the TypeSafe SDK for now.

Evaluation

On Ollama

Mean accuracy across Bespoke Labs’ 13 public datasets with human labels, 3,880 decisions in all. Tev1 and Nimble ran on Ollama. The Jev result is from Bespoke Labs’ own run of the same decisions through TypeSafe’s API.

Together AI results

These are Together AI’s development numbers for Tev1 4B, run on Together AI at temperature 0 with thinking off.

The same eval mix was used while the model was being built, so it isn’t an independent benchmark. The policy transfer set is built from synthetic policies.

Training data

Tev1 4B is a fine-tune of Qwen3.5-4B on 37,840 examples. Every source was converted into the same state, question, options format. Tev1 0.8B is a fine-tune of Qwen3.5-0.8B from the same project.

Tev1 takes its inspiration from Jev. None of its training data came from Jev.

Notes

• Use /v1/systemone. In a regular chat, Tev1 tends to reply in prose.
• Keep inputs short. Every question is scored with the full state and question set in its prompt, and Tev1 runs with a context of about 2,000 tokens. The longest training example is about 1,500 tokens.
• If none of your options might fit, add a none option.
• It can be wrong. Don’t let it be the only check on a high-stakes decision.
• Together AI hasn’t fully tested prompt injection, languages other than English, calibration, or how it handles inputs unlike its training data.
• Request bodies can be up to 64 KiB.

Reference

• Ollama blog
• How to train your own Jev for $17
• GitHub: togethercomputer/tev1
• Hugging Face: Tev1-4B-experimental
• Hugging Face: Tev1-0.8B-experimental