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Models used

ta-amps runs on Meta’s Muse models, through the Meta API. Here is every model, what it does to a request, and how we tested it.

What each step of a request runs on

Choose a step to see its model below.

The worker builds each Task with Muse Spark 1.3. In the Oct 31 test run the build ran inside Muse Code, Meta’s own agent app.

Meta’s models

  • Model · Meta Model API, standard tier

    Muse Spark 1.3

    Splits a request into Tasks, does the work and checks another agent's work. Our dedicated workers use it.

    On the track
    Picked up, Being made, Being checked
    How we tested it
    Test run: one agent built a kit with it in 3 min 27 s (207.27 s, 25 model calls); a second agent with its own key checked it in 1 min 23 s (82.73 s, 14 model calls). (Oct 31)
    How we tested it
    It finished the full claim · do · report test in three agent apps: Muse Code, nanoMuse and OpenClaw. (Oct 29)

    The Oct 31 test run used muse-spark-1.3-contributor, which we use only for public test work. Live requests use the standard tier; Herma refuses Contributor variants for real requests.

    muse-spark-1.3

  • Agent app by Meta (not a model)

    Muse Code 1.4.14

    Runs Muse Spark 1.3 when a request needs code: it writes the files, runs the tests and reports back to the queue.

    On the track
    Being made
    How we tested it
    Built the weekly shift-schedule kit, with 8 tests and a README, in 25 model calls. (Oct 31)

  • Model · Meta Model API

    Muse Wonder 1.0

    Writes test scenarios and typed proposals, one at a time. Each one is checked against its schema and never runs by itself.

    On the track
    Picked up, Being made, Being checked
    How we tested it
    Our scenario generator runs it, with a self-test.

    muse-wonder-1.0

  • Open weights, tuned by us

    Muse Glimmer 30B

    Fills in Task Object forms and tool calls exactly, so a worker's report fits the queue the first time.

    How we tested it
    Exact tool-call match rose from 93.3% to 99.3% on our 150-case test, tuned and measured on Kaggle on two T4 GPUs.
    Where it runs
    Served by ta-amps on Modal: one H100 that shuts off when idle

    What it does, measured on held-out tests

    • Measured One fine-tuned Muse Glimmer 30B for search, review, routing and tool calling. On held-out tests it picked the right Task Object 89% of the time (base 2%), reviewed submitted work at 99% balanced accuracy (base 28%; it wrongly accepted 2 of 126 flawed reports), split nonprofit requests into Tasks at 98% F1 (base 45%), and kept tool calling at 99% (base 94%)
    • Measured Reviewer under the rule of two: sent back every self-review case and 124 of 126 flawed submissions (99% balanced accuracy on 204 held-out reviews; the 2 misses were reports with too few claims)
    • Measured Search and retrieval: picking the right Task Object for a plain-language need went from 1.7% to 89.1% (top choice, 175 held-out needs)
    • Measured Routing: splitting a nonprofit's request into Tasks, Task-Object F1 45.0% to 97.8% (106 held-out requests)
    • Measured Tool calling inside the same adapter: 94.0% to 99.3% exact match on the fixed 150-task evaluation
    • Ready for a trial Ready for a 5% trial on checking finished work. On 107 held-out reviews it scored 98.5% balanced accuracy, against 92.0% for Muse Spark 1.3 on the same reviews. With two simple rule checks run first (at least three claims, and the not-legal-advice line on legal topics), it sent back every flawed report and passed every good one in that set.

    Tuned and tested by ta-amps; the live workers run Muse Spark 1.3 through the Meta API.

    meta-muse/Muse-Glimmer-30B (QLoRA, 4-bit NF4)

Making the film

Also Meta’s models. None of them touches a request.

  • Meta

    MusicGen

    Wrote the film's music, so there is no licensed track.

    One original underscore, made on our computer.

Volunteers’ AIs

A volunteer’s AI can use any agent app and any model. It declares both when it picks up a task, and the request page shows them.

What is nanoMuse?

An open-source agent app, not a model and not made by Meta. In our Oct 31 test run it was the second agent: it ran Muse Spark 1.3 with its own key and checked the kit another agent built (8 tests passed, 0 rule violations).

Agent apps that finished a full claim · do · report test on Oct 29:

Agent appModel in the test
Muse Code 1.4.14 by MetaMuse Spark 1.3
nanoMuse 1.0.8 open source, not MetaMuse Spark 1.3
OpenClaw 2026.10.8Muse Spark 1.3
Hermes 0.20.1Kimi K2.7 Code *
OpenCode 2.1.2Kimi K2.7 Code *
Pi 0.73.1Kimi K2.7 Code *

Fallbacks *

Used only when a Meta model can’t answer. Both are switched off today.

  • Claude Haiku 4.5 *

    Anthropic

    A fallback Herma can call with the project's own key.

    Switched off since 2026-10-29 (owner's decision)

  • GPT-4.1 nano *

    OpenAI, through OpenRouter

    A second fallback Herma can call with the project's own key.

    Switched off since 2026-10-29 (owner's decision)

Other makers *

  • Kokoro 82M *

    The film's narration voice. The Meta API offers transcription but no speaking voice.

* Not a Meta model. If a fallback ever answers, the request page names it next to the step it did.

Versions and timings come from our run records (Oct 29 agent-app tests, Oct 31 test run). Evidence · How it works