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0.1.0

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adapterci

Regression CI for fine-tuned local models. Scores travel, data doesn't.

Published27 Jul 2026
Direct dependencies2
Transitive dependenciesNot calculated

README

Version 0.1.0 documentation

AdapterCI

Regression CI for fine-tuned local models. Runs entirely against your own OpenAI-compatible endpoint (Ollama, LM Studio, llama.cpp, vLLM). Only metric names and floats are ever sent to the AdapterCI API — no prompts, no completions, no weights.

Install

pip install adapterci

Quickstart

adapterci init      # writes adapterci.yaml
# edit adapterci.yaml: api_key, project, endpoint, baseline_model, candidate_model
adapterci            # runs the suite, pushes scores, exits 1 on regression

Golden sets

Add domain-specific test cases in a local JSONL file (never uploaded):

{"prompt": "Summarize in one sentence: ...", "check": "contains", "expected": "..."}

check can be contains, regex, numeric_answer, or json_schema_keys.

Pointing at a different API (staging/local testing)

export ADAPTERCI_API_BASE=http://localhost:8787

License

MIT

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Declared dependencies 2

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2 direct targetsNot calculated transitive count
Package / targetRequirementRelationship
httpx
Environment
{
  "extras": "",
  "marker": null,
  "requires_python": ">=3.10",
  "evaluation": "NOT_EVALUATED"
}
httpx>=0.27runtime
pyyaml
Environment
{
  "extras": "",
  "marker": null,
  "requires_python": ">=3.10",
  "evaluation": "NOT_EVALUATED"
}
pyyaml>=6.0runtime

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2 collected
adapterci-0.1.0-py3-none-any.whl

bdist_wheel · 4.68 KB · Python py3

Source file
adapterci-0.1.0.tar.gz

sdist · 3.88 KB · Python source

Source file

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Observed since 20 Oct, 19:04 UTC

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package_version:c4c4b7a8c3d654e2797ec6a45bad555634df2c200e7f3fddc4e3298d1337bf6b

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