Lumma-Fev-0.1B

Lumma-Fev-0.1B is a decision model. It reads one document (the state) and a set of typed questions, and returns a probability distribution for each question in a single forward pass. It never generates text, so there is nothing to parse and nothing to hallucinate.

It is fine-tuned from FrontiersMind's Nandi-Mini-150M, which we pre-trained from scratch.

NOTE: Lumma-fev-4B and Lumma-feb-9B releasing on 26 September along with a detailed blog.

🎮 Lumma-fev in Action

🔥 See our smallest Lumma-fev 150 million model turn decisions into actions - playing Super Mario autonomously and in real time on a MacBook Air M2. ⚡

⚡ Despite its compact size, Lumma-fev can observe the game state, reason about the situation, and choose the next action — all running locally on consumer hardware. 💻

Benchmarks

Benchmarks

Model details

Use it with transformers

from transformers import AutoModel

model = AutoModel.from_pretrained("FrontiersMind/lumma-fev-0.1b", trust_remote_code=True)

answers = model.decide(
    state="My running shoes arrived in the wrong size. Can I exchange them for a size 10?",
    questions={
    "department": {
        "type": "choice",
        "instructions": "Which team should handle this?",
        "criteria": {
            "returns": "Exchanges, wrong items, damaged items",
            "shipping": "Delivery status, delays, lost packages",
            "billing": "Charges, invoices, payment problems",
        },
    }
}
)
print(answers)

state can be text, a JSON object or an array. Pin a version with revision="<commit>", and move the model to a GPU with model.to("cuda") (bf16 is the stored precision).

Use it with the lumma-fev package

pip install lumma-fev              # local inference
pip install "lumma-fev[serve]"     # plus the API server

import lumma_fev

model = lumma_fev.load("FrontiersMind/lumma-fev-0.1b")        # picks cuda, mps or cpu
print(model.decide("Two charges on my card for one order.",
                   {"billing": {"type": "noul", "instructions": "Is this about billing?"}}))

Serve it as an API

lumma-fev-serve exposes the TypeSafe POST /v1/systemone contract, so existing TypeSafe clients work by changing their base URL.

lumma-fev-serve --model FrontiersMind/lumma-fev-0.1b --host 0.0.0.0 --port 8000
# optional: LUMMA_FEV_API_KEY=<key> requires "Authorization: Bearer <key>"; --cors for browser apps

curl -s localhost:8000/v1/systemone -H 'content-type: application/json' -d '{
  "state": "I was charged twice. Please fix this ASAP.",
  "questions": {"billing": {"type": "noul", "instructions": "Is this ticket about billing?"}}
}'

From Python, with the lumma-fev client or the TypeSafe SDK:

from lumma_fev import Client

client = Client("http://127.0.0.1:8000")
print(client.decide("I was charged twice.", {"billing": {"type": "noul", "instructions": "Is this about billing?"}}))

from typesafe_sdk import Noul, TypeSafeClient     # pip install typesafe-sdk

with TypeSafeClient(api_key="local", base_url="http://127.0.0.1:8000") as ts:
    print(ts.system_one(state="I was charged twice.", questions={"billing": Noul(instructions="Is this about billing?")}).nouls["billing"].noul)

Answers

Questions never see each other: each one reads the state and its own instructions and options only, so one question cannot change another's answer. Text inside the request cannot forge the model's delimiter tokens.

Limits

• confidence and probabilities are the model's own estimates. Measure calibration on your own labelled data before gating automated actions on them.

Feedback

• Discord: https://discord.gg/ZGdjCdRt
• Email: support@frontiersmind.ai
• Website: https://www.frontiersmind.ai/
• LinkedIn: https://www.linkedin.com/company/frontiersmind/
• X (Twitter): https://x.com/FrontiersMind