galaxy-toolsmith 0.2.0

Library-first toolkit for Galaxy wrapper dataset, training, and generation workflows.

Galaxy Toolsmith

Galaxy Toolsmith is a library-first and CLI-driven toolkit for building datasets, training/evaluating model variants, and generating Galaxy tool wrappers.

Package and CLI

• Python package: galaxy_toolsmith
• CLI: gtsm

Quick start

gtsm doctor
gtsm init-workspace
gtsm sync-tools-iuc --ref main
gtsm sync-galaxy-skills --ref main
gtsm sync-galaxy-xsd --ref dev
gtsm extract-corpus --max-workers 8
# optional: disable README fetching from tool homepage URLs
# gtsm extract-corpus --max-workers 8 --no-fetch-docs
# optional: enrich records by running resolved containers for CLI help
# gtsm extract-corpus --max-workers 8 --resolve-containers --execute-containers
gtsm list-train-profiles
gtsm train --profile agentic-devstral-24b --corpus-jsonl .gtsm-cache/datasets/tools-iuc-corpus.jsonl
gtsm list-model-variants
gtsm export-model --variant-id bootstrap-dataset-agentic-devstral-24b --format all --quantizations q8_0,q6_k,q5_k_m,q4_k_m
gtsm estimate-model-resources
gtsm generate-wrapper --tool-name my_tool --help-text-file help.txt --output my_tool.xml
gtsm generate-wrapper --tool-name my_tool --help-text-file help.txt --artifact-format udt-yaml --output my_tool.yml
gtsm convert-udt --input my_tool.yml --output my_tool.xml --report my_tool.udt-conversion.json
gtsm evaluate-wrappers --wrappers my_tool.xml
gtsm evaluate-wrappers --artifact-format udt-yaml --wrappers my_tool.yml
gtsm evaluate-wrappers --wrappers my_tool.xml --run-planemo --run-planemo-tests --planemo-install-galaxy
gtsm benchmark-generate --corpus-jsonl .gtsm-cache/datasets/tools-iuc-corpus.jsonl --limit 50
gtsm promote-candidate --candidate-summary .gtsm-cache/runs/benchmark/benchmark.summary.json
gtsm list-promotion-policies

Test suites

make test
make lint

# Optional live Planemo/Galaxy smoke test. Use an existing Galaxy checkout or
# opt in to Planemo's Galaxy install behavior.
make test-optional-planemo PLANEMO_TEST_GALAXY_ROOT=/path/to/galaxy
make test-optional-planemo PLANEMO_TEST_INSTALL_GALAXY=1

# Optional live Ollama smoke test. Requires a running Ollama server and a real
# GGUF artifact path without whitespace.
make test-optional-ollama OLLAMA_TEST_GGUF=/absolute/path/model.gguf

The optional tests can also be run directly with pytest. They are skipped unless enabled with GTSM_TEST_LIVE_PLANEMO=1 or GTSM_TEST_LIVE_OLLAMA=1. The Ollama test accepts GTSM_TEST_OLLAMA_BIN when ollama is not on PATH.

Suggested gate before switching any default to DeepSeek:

1. Run benchmark-generate for current primary default profile output.
2. Run benchmark-generate for candidate DeepSeek profile output on the same corpus slice.
3. Run promote-candidate with baseline comparison and enforce your policy thresholds.

extract-corpus emits wrapper-level records (one per wrapper XML), includes normalized .shed.yml metadata (including suite context), writes a companion index file (*.index.json), and can enrich training records with command-line help collected from Singularity/Apptainer or Docker containers.

On Linux nodes, use the Makefile for the container-enabled path:

make install
make sync
make extract-corpus CONTAINER_RUNTIME=auto MAX_WORKERS=32
make train PROFILE=agentic-devstral-24b

If you already have a compatible conda environment active, use make install-active instead of creating .conda/gtsm.

export-model now supports multi-quant GGUF exports in one run with --quantizations.

For no-sudo GGUF export on shared Linux nodes, build and use a user-space llama.cpp quantizer instead of Unsloth's fallback installer:

make prepare-llama-cpp-export GGUF_EXPORT_ENV_DIR=.conda/gtsm-unsloth-export
make export-gguf-llama-cpp \
  GGUF_EXPORT_ENV_DIR=.conda/gtsm-unsloth-export \
  GGUF_VARIANT_ID=tools-iuc-qwen25-7b-full-20260617T221121Z-safe \
  GGUF_QUANTIZATIONS=q4_k_m
make finalize-gguf-export \
  GGUF_EXPORT_ENV_DIR=.conda/gtsm-unsloth-export \
  GGUF_VARIANT_ID=tools-iuc-qwen25-7b-full-20260617T221121Z-safe \
  GGUF_RUN_TAG=20260617T221121Z

The helper configures llama.cpp with OpenMP disabled to avoid conda libgomp linker issues, then runs export-model with GTSM_GGUF_BACKEND=llama.cpp. The finalize target verifies the produced GGUF, refreshes the overnight export JSON, and writes an Ollama Modelfile without running ollama create unless GGUF_OLLAMA_CREATE=1 is set.

Training defaults are accessibility-first and now make quantization state explicit in list-train-profiles. Recommended flow is to fine-tune non-quantized profiles first, then export quantized variants; optional pre-quantized profiles (*-4bit) remain available.

DeepSeek profiles are included as opt-in evaluation defaults (coding/distilled variants). Keep primary defaults on the existing Devstral/Mistral set unless DeepSeek variants pass your benchmark and promotion gates.

Model download/source behavior can be controlled with:

• GTSM_MODEL_SOURCE_REGISTRY (or HF_ENDPOINT) for registry endpoint
• GTSM_MODEL_REVISION for pinned model revision
• GTSM_MODEL_CACHE_ROOT (or HF_HOME) for cache location; defaults to .gtsm-cache/models/hf-cache
• GTSM_MODEL_LOCAL_FILES_ONLY=true for offline/local-only loading

Inspect resolved cache settings with:

gtsm model-cache-info

Set HF_TOKEN when available to avoid unauthenticated Hugging Face Hub rate limits; cached local weights are reused either way after the first successful download.

Optional server/client mode

gtsm serve --host 127.0.0.1 --port 8765 --provider local
gtsm generate-wrapper-remote --server-url http://127.0.0.1:8765 --tool-name my_tool --help-text-file help.txt --output my_tool.xml
# inspect available model variants served by FastAPI:
curl http://127.0.0.1:8765/variants
# open browser monitoring dashboard:
# http://127.0.0.1:8765/monitor

Set GTSM_SERVER_AUTH_TOKEN to require/send bearer auth for remote generation.

Distributed training server/worker mode

# start server (set one or more tokens to lock down training/artifact endpoints)
gtsm serve --host 127.0.0.1 --port 8765 --auth-token "$GTSM_SERVER_AUTH_TOKEN"

# submit training job to coordinator
gtsm train-remote-submit --server-url http://127.0.0.1:8765 --profile agentic-devstral-24b --dataset-manifest config/dataset.manifest.json --corpus-jsonl .gtsm-cache/datasets/tools-iuc-corpus.jsonl

# run one or more workers
gtsm train-worker --server-url http://127.0.0.1:8765

# fetch produced artifacts in parallel
gtsm train-artifacts-fetch --server-url http://127.0.0.1:8765 --job-id <job-id> --output-dir .gtsm-cache/models/remote-artifacts --max-workers 4

External generation providers

• --provider openai: uses GTSM_OPENAI_API_KEY and optional GTSM_OPENAI_BASE_URL
• --provider anthropic: uses GTSM_ANTHROPIC_API_KEY and optional GTSM_ANTHROPIC_BASE_URL
• --provider copilot: uses GTSM_COPILOT_API_KEY and optional GTSM_COPILOT_BASE_URL
• --provider ollama: uses GTSM_OLLAMA_BASE_URL and optional GTSM_OLLAMA_MODEL

Ollama export/deployment helpers

• Generate Modelfile from exported GGUF:
  • gtsm export-ollama-model --variant-id <variant-id> --model-name <name>
• Optionally register model in local Ollama runtime:
  • gtsm export-ollama-model --variant-id <variant-id> --model-name <name> --create
• Optional post-training hook:
  • gtsm train ... --post-export-quantizations q8_0,q6_k,q5_k_m,q4_k_m --post-ollama-model-name <name> [--post-ollama-create]

Optional local unsloth provider

• Set GTSM_LOCAL_UNSLOTH_MODEL to enable local unsloth inference in --provider local
• Optionally set GTSM_LOCAL_UNSLOTH_ADAPTER to load a LoRA adapter path
• GTSM_LOCAL_GENERATOR_CMD still takes precedence when set (external command mode)

Documentation

• Build docs locally:
  • python -m pip install -r docs/requirements.txt
  • python -m mkdocs build --strict
  • python -m mkdocs serve -a 127.0.0.1:8000
• Production docs deployment uses Cloudflare Workers static assets configured in wrangler.jsonc.
• Full 4xA100 pipeline runbook: docs/example.md