Strands decider is a small, fast decision model for agentic AI. Unlike an LLM, which can generate arbitrary text, a decision model picks between sets of options and rates things on a scale, and every answer carries a calibrated confidence. It fits the decisions inside an agentic workflow: model routing, tool selection, argument checking, triage, guardrails, evaluations, and the rote decisions of a hybrid agent that leaves the hard ones to an LLM.
This is v1, the first Strands decider on Gemma 4, on google/gemma-4-E4B-it. It trains on hobson-v21's data (123,339 rows) plus 12,263 code-task rows. The teacher is Qwen/Qwen3.5-4B: its output distributions are training targets where its answer agrees with the gold label. A frozen-KL anchor keeps the answers close to those of StrandsAgents/strands-decider-2B-hobson-v19. The checkpoint is a soup of three seeds of this recipe: each layer's LoRA update is the exact mean of the seeds' updates (stored as one rank-48 adapter), and the score of an option is the mean of the three heads' scores. The soup was then calibrated.
Model names:strands-decider-<size>-<base>-v<N>-<YYMM>, that is the base model's size, the base model family, the release on that family (v1 is the first) and the release month (docs/naming.md in the code repository).
This repository holds a LoRA adapter on google/gemma-4-E4B-it plus a small readout head that scores the options of a typed question (noul, a yes/no question; choice, one of N options; score, a level on an ordered scale). The code, the training recipe, the data inventory and the evaluations are at https://github.com/strands-labs/strands-decider, under the same Apache-2.0 license as this model (see LICENSE.md).
This installs the code repository's main, which has Gemma 4 support; the package on PyPI does not have it yet.
Ask one or more questions about a state. Pass --device cuda, mps or cpu; without it, the best available device is used. The base weights download from the Hub at first use.
strands-decider ask StrandsAgents/strands-decider-E4B-gemma4-v1-2610 \
--state "Help! My payouts have been failing for 3 days! " \
--choice "Which team should handle this?=billing,sales,retail" \
--noul "Does this convey urgency?" \
--score "How frustrated is the writer?=calm,frustrated,depressed"
The output of this checkpoint (--device cpu; other devices differ in the last digits):
Or serve it over HTTP. The server binds to 127.0.0.1 and has no authentication: use it for local experiments.
strands-decider serve StrandsAgents/strands-decider-E4B-gemma4-v1-2610 --port 8000
curl -s localhost:8000/v1/systemone -H 'content-type: application/json' -d '{
"state": "Help! My payouts have been failing for 3 days!",
"questions": {"is_urgent": {"type": "noul", "instructions": "Does this convey urgency?"}}
}'
In Python, strands_decider.modeling.StrandsDeciderModel.load("StrandsAgents/strands-decider-E4B-gemma4-v1-2610"). The adapter in lora/ is a standard PEFT adapter on the Gemma 4 text decoder (transformers.Gemma4ForConditionalGeneration(...).model.language_model); the head is head.safetensors.
Decision Index 0.2.1, balanced skill (window 32,768)
39.42
internal set
accuracy
n
eval: held-out short tasks
0.684
6,000
eval: boardgame
0.844
900
eval: contractnli
0.872
1,026
eval: hotpotqa (held out)
0.829
959
eval: musique
0.922
1,199
eval: musique, answerable
0.922
599
eval: musique, unanswerable
0.923
600
eval: [4] adequacy_hs2
0.786
234
eval: [5] gen:adequacy
0.838
302
eval: [6] code/bug_swapped_operands
0.843
89
eval: [6] code/bug_variable_misuse
0.730
89
eval: [6] code/bug_wrong_binary_operator
0.719
89
eval: [6] code/commit_message
0.989
89
eval: [6] code/correct_solution
0.371
89
eval: [6] code/cwe_choice
0.933
89
eval: [6] code/docstring_match
0.955
89
eval: [6] code/exception_type
0.787
89
eval: [6] code/exec_input
0.782
142
eval: [6] code/exec_output
0.775
662
eval: [6] code/needle_function
0.974
391
eval: [6] code/review_needed
0.652
89
JevBench is the external benchmark (231 public tasks); the Decision Index is the decision-model index of the decision-index kit. The internal sets are this recipe's held-out short tasks, multi-step documents, answer-adequacy judgements and code tasks. evaluation/README.md in the code repository describes each set. Per-skill results and the raw reports and logs are in eval/summary.json and eval/internal/.
Questions are read less than documents. With the state and options fixed, a changed question often gets the same answer. Phrase a question so the obvious reading is the intended one.
Long, multi-step documents are the weak spot: JevBench's hard tier scores far below its easy tier.
score and noul transfer poorly to rubrics and yes/no tasks unlike the training mix. Train on a rubric resembling yours.
Calibration is one temperature per primitive, fitted on held-out short classification. The confidence bands are established there only: measure on your own traffic before you trust a threshold.
Trained on public datasets, so it inherits their domains and their label noise.
evaluation/README.md, section "Limitations", has the measured figures behind each point.
The datasets list in the metadata holds the 25 Hub datasets that the training rows come from: 18 short-task classification datasets, tasksource/Boardgame-QA, nvidia/HelpSteer2, and five code datasets. The other sources are:
ContractNLI and MuSiQue, from their authors' releases (not from the Hub).
Synthetic rows that open-weight language models generated and checked.
Code-task rows built here: generated programs with execution-checked answers, and functions from permissively licensed GitHub repositories. The five code datasets give the other code tasks (CuBERT ETH Py150 Open, CommitPackFT, CodeReviewer, Juliet C/C++ 1.3, CodeContests). Code rows carry gold labels only.
The output distributions of the frozen teacher model Qwen/Qwen3.5-4B. They are training targets, next to an anchor toward the distributions of StrandsAgents/strands-decider-2B-hobson-v19.
These Hub datasets are for calibration and evaluation only, not training: dair-ai/emotion, mteb/amazon_massive_intent, raquiba/Sarcasm_News_Headline, ucberkeley-dlab/measuring-hate-speech and hotpotqa/hotpot_qa.
In the code repository, data/sources.md lists every source with its revision, role, license and attribution. data/README.md states what is committed, what is downloaded and the reproduction contract. Read a source's own terms before you redistribute data built from it.
Three seeds (0, 1, 2) of one config (train_config.json; the seeds differ only in seed), each trained by training/recipe.sh train on one host with 8 NVIDIA H100 80GB GPUs (NGPU=8), one epoch of 4,109 steps. The soup was made by strands-decider soup and calibrated and evaluated by training/recipe.sh calibrate eval. training/README.md has the setup, the stages and the hardware notes.
provenance.json: base model and revision (inferred: the hosts did not pin one), and the sha256 of the config, adapter, head and source pickle. MANIFEST.sha256 lists every file; python -m strands_decider.hf_export verify <folder> checks them. The run records keep their timings and results; host paths, cloud identifiers and cost fields are removed.
onnx-community