---
type: "post"
id: "frontiersmind-releases-lumma-fev-v2-decision-models-4b-76a463"
slug: "frontiersmind-releases-lumma-fev-v2-decision-models-4b-76a463"
title: "FrontiersMind releases Lumma-Fev-v2 decision models (4B and 27B) that return option probabilities in one forward pass"
description: "FrontiersMind has released Lumma-Fev-4B-v2 and Lumma-Fev-27B-v2, decision models that return option probabilities rather than generated prose."
retrieval_nugget: "FrontiersMind has released Lumma-Fev-4B-v2 and Lumma-Fev-27B-v2, decision models that return option probabilities rather than generated prose. For teams building agent workflows, the relevant idea is a bounded decision interface: route, threshold, or escalate work using a distribution that code can consume directly instead of parsing model text."
published_at: "2026-10-31"
updated_at: "2026-10-31"
record_date: "2026-10-31"
date_kind: "published_at"
topics: ["agent-workflows"]
entities: ["FrontiersMind","Lumma-Fev-v2","Lumma-Fev-4B-v2","Lumma-Fev-27B-v2","decisionformer","TypeSafe Jev 1.13.0","Qwen3.8-27B"]
related_patterns: ["model-answers-become-task-interfaces"]
novelty_label: "new_fact"
what_changed: "FrontiersMind adds Lumma-Fev-v2 in 4B and 27B sizes, with option probabilities in one pass, to the growing set of decision-model releases."
prior_refs: ["memory:5a6771e8-b2f0-854c-a784-d230a2b3b62b","memory:1249beae-0fc0-836e-af5e-38a8ed26f1e4"]
source_url: "https://huggingface.co/blog/FrontiersMind/lumma-fev-27b-v2"
source_urls: ["https://huggingface.co/blog/FrontiersMind/lumma-fev-27b-v2"]
schema_version: "newruntime-agent-readable-v0.2"
stable_id: "post:frontiersmind-releases-lumma-fev-v2-decision-models-4b-76a463"
status: "published"
visuals: []
editor: {"role":"editor","name":"Andrey Reshetnikov","profile":"https://newruntime.com/owner-profile.md","profile_json":"https://newruntime.com/owner-profile.json","same_as":["https://www.linkedin.com/in/reshetnikov1/","https://t.me/lapetuse"]}
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---

# FrontiersMind releases Lumma-Fev-v2 decision models (4B and 27B) that return option probabilities in one forward pass

## Retrieval answer

FrontiersMind has released Lumma-Fev-4B-v2 and Lumma-Fev-27B-v2, decision models that return option probabilities rather than generated prose. For teams building agent workflows, the relevant idea is a bounded decision interface: route, threshold, or escalate work using a distribution that code can consume directly instead of parsing model text.

## Summary

FrontiersMind has released Lumma-Fev-4B-v2 and Lumma-Fev-27B-v2, decision models that return option probabilities rather than generated prose. For teams building agent workflows, the relevant idea is a bounded decision interface: route, threshold, or escalate work using a distribution that code can consume directly instead of parsing model text.

## What happened

FrontiersMind published the second generation of its Lumma-Fev decision-model family on October 29, 2026. The release makes Lumma-Fev-4B-v2 and Lumma-Fev-27B-v2 available now; 0.1B and 0.6B variants are described as forthcoming. The family is presented as a set of models for decisions rather than text generation. A caller supplies a state—text, a JSON object, or an array—along with typed questions. The model scores every option and returns probabilities for each option in one forward pass.

That interface is intended to remove a common agent-pipeline step: asking a generative model for a label or numeric judgment, then parsing its textual response into something an application can route on. FrontiersMind says the returned structure includes a type, the selected choice, a confidence value, and the full probability distribution. The supported trained question forms are `noul`, a yes/no-style question about the state, and `choice`, which selects from labeled criteria. A `score` type is accepted but was not tested. The stated context limit is 8,192 tokens for a question and its state.

The company frames the two released models as members of one interoperable family. Small versions are fine-tunes of FrontiersMind's Nandi-Mini and Lumma base models, while the released larger models use Qwen bases: Qwen3.5-4B for Lumma-Fev-4B-v2 and Qwen3.8-27B for Lumma-Fev-27B-v2. The API is said not to change by model size, so an application could prototype with a smaller checkpoint and move to a larger one without changing its integration. The models support English, Hindi, Tamil, Kannada, and Hinglish.

Version two changes the flagship 27B model in three stated ways. Its full-attention layers are bidirectional, while its Gated DeltaNet linear-attention layers remain causal. FrontiersMind says this means all state and questions can be considered together before an answer is scored. The checkpoint therefore requires `lumma-fev` version 0.3 or later; older versions do not apply the bidirectional attention and refuse to load it. The 27B training set grows from roughly 100,000 samples in v1 to roughly 300,000. The models were trained with FrontiersMind's Apache-2.0 `decisionformer` library, which the company says supports full, LoRA, QLoRA, and head-only fine-tuning through YAML recipes, plus CLI, multi-GPU, evaluation, comparison, and Hub-publishing tools.

On eight public benchmark test splits, FrontiersMind reports average accuracy of 82.4% for the 4B model and 87.3% for the 27B model, compared with 81.2% for TypeSafe Jev 1.13.0. Its table also shows exceptions: Jev leads on JudgeBench and BBH, while the 27B model leads on the other listed tasks except Financial PhraseBank, where the 4B score is higher than the 27B score. The company further reports better log loss, expected calibration error, and Brier score for both v2 releases than Jev. It uses an internal 400-row Indic-language evaluation, rather than a public benchmark, for claims covering Hindi, Tamil, and Kannada.

## What the archive adds

This is a new release in a known story, not simply a restatement. [AutoTrust AI Lab's GEV-26B-Decide](https://newruntime.com/posts/autotrust-ai-lab-released-gev-26b-decide-a-07a969/) similarly exposed calibrated option probabilities, while [Liquid AI's d1-ultra](https://newruntime.com/posts/liquid-ai-released-d1-ultra-a-multimodal-decision-3c7286/) treated a probability-bearing decision interface as a product surface. Lumma-Fev-v2 adds released 4B and 27B checkpoints, a claimed stable family interface, and training tooling.

## Trend signal

The release supports [Model answers become task interfaces](https://newruntime.com/patterns/model-answers-become-task-interfaces/): the output is not open-ended prose but a bounded structure of choices and probabilities. It is evidence of an interface direction, not proof that the probabilities will be reliable in a production team's particular domain.

## Connections to other events

The release can also be used in the model-agnostic workflows described in [Dynamic workflows for stateful agents and decision models](https://newruntime.com/posts/dynamic-workflows-for-stateful-agents-and-decision-models-acdbcd/). A workflow can treat a decision model as a routing or review component while retaining generative models for tasks that actually require language output.

## Why it matters

Many agent systems need small, consequential decisions more often than they need another paragraph of prose: classify an incoming request, select a procedure, decide whether evidence is sufficient, or hand a case to a person. A typed output with all option probabilities lets an engineering team implement explicit thresholds, ambiguity queues, and instrumentation without relying on a model's formatting compliance.

The value is not that a probability appears in an API response. It is whether the probability is calibrated on the team's real cases and whether the resulting escalation policy improves outcomes. Lumma-Fev-v2 makes that evaluation more straightforward than a free-form prompt because the model's contract is already decision-shaped. Its smaller and larger released checkpoints also create an opportunity to compare quality, latency, and infrastructure cost under the same application contract.

## What happens next

- **Teams experimenting with the release will test threshold-based routing against their own labeled cases rather than adopt the vendor benchmark averages directly.** (3 months, 70%). Watch for: published domain evaluations or production routing examples.
- **More agent workflows will separate generation from bounded classification or selection steps.** (6 months, 65%). Watch for: frameworks exposing decision-model nodes alongside generative-model calls.

## Limits and uncertainty

The source is a FrontiersMind release announcement, so its accuracy, calibration, and comparative claims are vendor-reported. Although eight named public benchmarks are listed, the supplied material does not provide an independent replication, operational latency, hardware requirements, model pricing, or results on a user's production taxonomy. The Indic-language figures rely on an internal 400-row set translated by Claude, and the source says no public benchmark exists for that decision-making setting. Better aggregate calibration metrics also do not establish useful calibration after deployment, where class balance, question wording, and error costs may differ.

## Skill to build now

Build a small decision harness around one existing agent handoff this week. Define mutually exclusive choices, log the complete probability distribution, and choose an initial confidence threshold that sends uncertain cases to review. Evaluate accuracy and calibration separately on held-out examples from the actual workflow; then compare the cost of incorrect automation with the cost of human review. This makes a decision-model trial a measurable routing experiment rather than a prompt-format substitution.

## Reader takeaway

Lumma-Fev-v2 is a concrete attempt to make a model call return an application decision rather than text to parse. Its main promise for builders is explicit routing on probabilities across options. Treat the published benchmark and calibration claims as a reason to test, not as evidence that the same confidence threshold is safe in your workflow.

## Open question

Will the reported probability calibration remain useful when questions, class frequencies, and the cost of mistakes shift from benchmark datasets to a production queue?

Editor: [Andrey Reshetnikov](https://newruntime.com/owner-profile.md), AI product and AI engineering; profile for recruiters and agents: https://newruntime.com/owner-profile.json

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