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drummer2-tools

by Luke Steuber lukeslp/drummer2-tools

drummer2-tools is an open-weight model from Luke Steuber, released under Apache License 2.0. Its published files total 1.1 GB.

This repository contains Tools 0.10.0, a narrow tool-proposal fine-tune of the 542M-parameter Drummer base at 10.85B pretraining tokens. It is trained for a fixed catalog of tools. It is a research model, not a general agent and not a product.

Parameters—
Context—
Weights1.1 GB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads—

Model Card

By Luke Steuber, published under apache-2.0, revision 34a0edfaba4a.

This repository contains Tools 0.10.0, a narrow tool-proposal fine-tune of the 542M-parameter Drummer base at 10.85B pretraining tokens. It is trained for a fixed catalog of tools. It is a research model, not a general agent and not a product. - Three passes, 204 updates, 1,656,936 supervised target tokens; learning rate 1e-4. - The corpus is execution-verified against the tool executor. It teaches six local tools and four live web tools plus refusal/clarification behavior. Tool results are not model-written facts. The held-out result is the central limitation. Fine-tuned on the same rows, Qwen2.5-0.5B-Instruct made a valid call naming the unseen tool in 11/24 and SmolLM2-360M-Instruct in…

Read Luke Steuber's full model card

Drummer-540M Tools 0.10.0

This repository contains Tools 0.10.0, a narrow tool-proposal fine-tune of the 542M-parameter Drummer base at 10.85B pretraining tokens. It is trained for a fixed catalog of tools. It is a research model, not a general agent and not a product.

Training

  • Parent: the completed 542M base checkpoint at 10.85B tokens (drummer2-540m-100pct-step-82750), not the later continued Foundation 0.12.0 checkpoint.
  • Corpus: tools-declared-v6, 11,054 records; profile declared-v2.
  • Three passes, 204 updates, 1,656,936 supervised target tokens; learning rate 1e-4.
  • The corpus is execution-verified against the tool executor. It teaches six local tools and four live web tools plus refusal/clarification behavior. Tool results are not model-written facts.

Results

Measure Tools 0.10.0 Comparison / note
Sealed execution test 116/120 (96.7%) Tools 0.9.0: 107/120
Live web tools 56/60 (93%) Tools 0.9.0: 55/60
Prohibited-tool calls 0/100 no false calls in this set
Transfer-v2 total 70/96 prior model: 43/96
Held-out tools declared in the prompt but absent from training 0/24 prior model: 0/24
Last-N-character probe 16/16 prior model: 0/16
Clarification 10/12 prior model: 6/12
Precision at coverage, transfer test 1.000 at 0.647 coverage prior model: 0.677 at 0.456
p95 latency, Apple-GPU (MLX) serving path 1.2 s recorded qualification run

The held-out result is the central limitation. Fine-tuned on the same rows, Qwen2.5-0.5B-Instruct made a valid call naming the unseen tool in 11/24 and SmolLM2-360M-Instruct in 10/24; Drummer made no valid selection (0/24). Exact-and-executed scores were 7/24, 8/24 and 0/24 respectively. Qwen2.5-0.5B base, with no instruction tuning, made a valid call naming the unseen tool in 8/24 and executed 7/24. OPT-350M, Pythia-410M and BLOOM-560M controls, pretrained on 180B to 350B tokens, also made no valid selection (0/24); counting the name in raw output they emitted it 2, 8 and 10 times, every one invalid.

A preregistered function-masking experiment replaced every training example's tool and argument identifiers with fresh opaque aliases. The primary result was 0/24 held-out tools: masking did not recover declaration reading. It also decisively harmed trained-tool execution, which fell from 116/120 to 88/120 (31 paired losses, 3 gains; p = 7.7e-07); invalid responses rose from 2 to 21, precision fell from 0.971 to 0.784 and coverage from 0.972 to 0.708. A post-hoc 4-to-9 shift in correct-name emissions (invalid calls that name the right tool) was unregistered, one-seed evidence from five discordant pairs (one-sided p = 0.0312, two-sided 0.0625); the same signature appears without masking in the sub-350B reference arms, so it reads as more copying, not recovered capability. Across the tested model families, the observed capability boundary lies within the 350B-to-roughly-4T pretraining interval; this does not prove a universal scaling threshold or decoder-architecture limit.

Use

Use this checkpoint only for research on fixed-catalog tool calling, execution-verified training and the difference between memorizing a tool catalog and reading a novel declaration. The package uses Drummer's inference_v2 format and includes its tokenizer and manifest.

Limitations and safety

  • No safety tuning. Do not grant it unsupervised access to external systems or consequential tools.
  • It is good at the exact trained catalog and fails to use unseen tools from prompt declarations: 0/24 on the sealed test.
  • It can propose wrong tools or arguments. Validate every proposal against a strict allowlist and schema, execute at most once, and keep side effects behind human confirmation.
  • It invents facts and should not answer factual questions from its weights. Use deterministic tools and quote tool outputs.
  • The 10.85B-token Foundation 0.11.0 parent scored 0.257 on MMLU, chance level. General knowledge and reasoning are weak.
  • Context length is 2,048 tokens. The model is primarily English.
  • This repository is not the continued 15.85B-token foundation. It is fine-tuned from the earlier 10.85B base.

License and author

Weights and code: Apache-2.0. Card and project-authored documentation: CC BY 4.0. Training-source terms remain as documented for the Drummer base (https://huggingface.co/lukeslp/drummer2-540m). About 8% of that base's tokens were smol-smoltalk, text generated by Llama-3.1-405B; that model's license includes a naming clause, and this fine-tune inherits the disclosure. The function-masking design follows Lin et al., 2024 (Hammer, arXiv:2410.04587).

Luke Steuber - lukesteuber.com - GitHub

Full evidence and live demo: https://drummer-demo.dr.eamer.dev

Identity and Version

Repository
lukeslp/drummer2-tools
Publisher
Luke Steuber
Task
Not stated by the source
Modality
Other
Library
Not stated by the source
Parameters
Not stated by the source
Languages
en
Revision
34a0edfaba4afdad77db0089625cc14bd6798142
First published
2026-09-21
Last updated
2026-09-27

Files and Weights

7 files, 1.1 GB in total. The weights are 1 file totalling 1.1 GB in safetensors.

Weights1 file · 1.1 GB
Configuration3 files · 21.6 KB
Tokenizer1 file · 506.6 KB
Documentation1 file · 5.0 KB
Repository1 file · 1.5 KB
Every file
FileTypeSizeSHA-256
model/model.safetensorsWeights1.1 GB 6b046c948d12
inference_v2.jsonConfiguration632 B —
model/config.jsonConfiguration1.0 KB —
model/manifest.jsonConfiguration19.9 KB —
README.mdDocumentation5.0 KB —
.gitattributesRepository1.5 KB —
tokenizer.jsonTokenizer506.6 KB —

License and Download

License
apache-2.0
Access
Open weights, no gate
Download size
1.1 GB
Download from Luke Steuber

Released by Luke Steuber through its official repository on Hugging Face. Read the license.

Built From

  • Described by arXiv:2410.04587

Memory Requirements

PrecisionWeights in memory
As published1.1 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About drummer2-tools

Can I use drummer2-tools commercially?

Yes. drummer2-tools is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.