Muse-Glimmer-30B is sold as an agentic model, and it is not short of knowledge about the tools you hand it. Probe the stock model on held-out agentic trajectories and it will name the correct function verbatim in its own reasoning — and then keep deliberating past the point where an agent loop needed a call. Here is the stock model, mid-thought, on a task whose gold action is databutton-submit_app_requirements:

"We need to submit app requirements via databutton-submit_app_requirements. Need name, pitch, sp…"

Correct tool, correctly named, never emitted. The generation budget ran out first. That is not a knowledge gap, it is a decisiveness and format gap, and it is the single thing standing between this model and a working agent loop. On 200 held-out examples the stock model produced a well-formed call only 98.3% of the time when one was required.

Supervised finetuning on real trajectories is the direct fix, and it costs 191.7M trainable parameters — 0.64% of the model.

Results

Toucan held-out (in-distribution), n=200	stock	+ adapter	change
named the right tool anywhere in its output	92.4%	91.6%	-0.8pp (no significant change)
emitted a well-formed call when one was needed	98.3%	90.8%	↓ -7.6pp worse
tool-name accuracy	85.7%	88.2%	+2.5pp (no significant change)
argument exact match	61.3%	72.3%	↑ +10.9pp better
schema valid (name exists, required args present)	97.5%	90.8%	↓ -6.7pp worse
abstained when no tool applies	85.2%	100.0%	↑ +14.8pp better
hit the generation ceiling mid-answer	1.7%	1.7%	— +0.0pp

BFCL v3 (not trained on), n=120/subset	stock	+ adapter	change
overall AST accuracy	81.2%	85.2%	↑ +4.0pp better
live irrelevance	77.5%	83.3%	↑ +5.8pp better
live multiple	78.3%	78.3%	— +0.0pp
live simple	72.5%	76.7%	↑ +4.2pp better
multiple	81.7%	90.0%	↑ +8.3pp better
simple	92.5%	95.8%	↑ +3.3pp better

BFCL v3 is the honest test here. Every Toucan trajectory was generated by MiniMax-M2.5, so gains against Toucan gold are partly gains at imitating that model. BFCL was never trained on.

Does it break anything?

A tool-calling finetune damages a general model in one characteristic way: the model starts emitting call syntax when no tools were offered at all. That failure is invisible to every metric above, so it is measured directly — GSM8K prompts with tools=None, counting any <atem:invoke in the output.

GSM8K, no tools offered, n=200	stock	+ adapter	change
accuracy	74.0%	88.0%	↑ +14.0pp better
tool-syntax intrusion	0.0%	0.0%	— +0.0pp
MMLU (n=250)	79.6%	81.6%	↑ +2.0pp better

Paired significance on the GSM8K delta — McNemar: 32 fixed by the adapter, 4 broken by it, exact two-sided p = 0.0000 -- significant at 0.05.

Usage

python

from transformers import AutoModelForImageTextToText, AutoTokenizer
from peft import PeftModel

# transformers >= 5.15 is required: `muse_glimmer` is not a registered model
# type before that, and the checkpoint carries no remote code.
base = AutoModelForImageTextToText.from_pretrained(
    "meta-models/Muse-Glimmer-30B", dtype="bfloat16", device_map="auto")
model = PeftModel.from_pretrained(base, "PursuitOfDataScience/Muse-Glimmer-30B-ToolCall-LoRA")
tok = AutoTokenizer.from_pretrained("meta-models/Muse-Glimmer-30B")

msgs = [{"role": "user", "content": "What's the weather in Chicago?"}]
tools = [{"type": "function", "function": {
    "name": "get_weather",
    "description": "Current weather for a city.",
    "parameters": {"type": "object",
                   "properties": {"city": {"type": "string"}},
                   "required": ["city"]}}}]
ids = tok.apply_chat_template(msgs, tools=tools, add_generation_prompt=True,
                              return_tensors="pt").to(model.device)
print(tok.decode(model.generate(ids, max_new_tokens=256)[0, ids.shape[1]:]))

The model answers in Muse-Glimmer's native ATEM syntax, on the to= recipient channel — the adapter does not change the interface, only the reliability of using it.

Training


Data	44,800 examples: 40,000 Toucan-1.5M trajectories + 4,800 manufactured abstention hard negatives
Steps	700 x 24 examples/step
Hardware	3x H100 NVL 94GB, 669 GPU-minutes over 14 one-hour slices
LoRA	r=32, alpha=64, dropout=0.05, text tower only
Targets	`model.language_model.layers.\d+.(self_attn.(q
LR	0.0001 cosine to 1e-05, warmup 40, grad-clip 1.0
Sequence	max 4096 tokens, micro-batches capped at 6144 padded tokens
Loss	assistant spans only, masked by re-rendering each message prefix

The perception encoder and vision adapter are frozen. This corpus is text; adapting a ViT on text trajectories damages the multimodal path for nothing.

Data preparation

Three defects in the raw corpus are filtered, because each teaches the opposite of the goal:

7.4% of records leak Toucan's own <tool_call>{...}</tool_call> JSON into assistant prose — a third syntax this model's template never uses. Lifted into structured calls.
4.2% of gold calls name a tool that was never offered; 114 are literally named unknown. Dropped — supervising those teaches exactly the hallucination the finetune is meant to remove.
thinking_cot interleaves the agent's planning with [Tool Simulation — …] blocks, which are the data generator inventing tool results. Only the agent passes are kept; an agent must never be trained to hallucinate observations.

Validation is deduplicated against training by normalised opening prompt. Toucan's subsets overlap by construction — single-turn-diversify rewrites the same seeds as single-turn-original — so distinct uuids are not distinct problems, and a naive split leaks 4.6% of validation.

The abstention hard negatives are manufactured from training data at zero labelling cost: take a trajectory, delete the tool it needs, keep the question, and expect an honest refusal. Over-calling is the failure that actually hurts a deployed agent, and Toucan's own irrelevant subset is small.

Provenance and licensing

Base model Muse-Glimmer-30B, Apache-2.0. Meta also publishes a USAGE_POLICY.md acceptable-use policy which travels with any derivative.
Training data Toucan-1.5M, Apache-2.0.
Toucan is synthetic: every trajectory used here was generated by MiniMax-M2.5. The behaviour this adapter installs is distilled from that model's outputs and inherits its habits. BFCL is reported precisely because it is not from that distribution.

Limitations

Trained for 700 steps on 16,800 examples. Validation loss was still falling when the step budget ran out (0.5038 at step 150 -> 0.4753 at step 700), so this is not a converged finetune.
The tool-calling evaluation scores the first call of a trajectory. It does not measure multi-turn task completion.
Muse-Glimmer is multimodal. The vision tower was frozen, but the language tower was adapted and image understanding was not re-measured. Treat the multimodal path as untested after this adapter.
Single seed. Small differences between arms are not meaningful; the paired test above is reported for exactly that reason.
Sampling error is roughly ±6.9 points per cell at these n.