Reproducibility

Language models generate text by sampling, so left to their providers' defaults two runs over the same input can return different skills. LAiSER is configured so that, by default, they do not.

Defaults

Every backend decodes greedily: at temperature 0.0 the model always takes its most probable next token, so the same prompt produces the same text. A fixed seed (42) is also supplied wherever a backend accepts one, which keeps runs reproducible if you deliberately raise the temperature.

Backend	Temperature	Seed
Gemini	0.0	42, on google-genai releases that accept a seed
OpenAI	0.0	not accepted by the API
Local model through Transformers	greedy — sampling switched off	42, used when temperature is above 0.0
Local model through vLLM	0.0	42
llama.cpp	0.0	42, on llama-cpp-python releases that accept a seed

For Transformers models, sampling is turned off explicitly rather than left to the checkpoint. Some checkpoints ship a generation_config.json that enables sampling, which would otherwise make repeated runs differ.

Alignment is deterministic as well. Each extracted phrase is embedded with a fixed sentence-transformer model and matched by exact search against the bundled taxonomy index, so the same phrase always resolves to the same taxonomy entry with the same similarity score.

Changing the defaults

Pass temperature and seed when creating an extractor:

extractor = SkillExtractorRefactored(model_id="gemini", api_key=api_key, temperature=0.7, seed=123)

Pass seed=None to send no seed. The defaults can also be changed for a whole process through environment variables, which LAiSER reads when it is imported:

Variable	Default	Effect
LAISER_TEMPERATURE	0.0	default temperature for every backend
LAISER_TOP_P	1.0	nucleus sampling cut-off; 1.0 leaves it off

Reference configuration

To let others reproduce your results, pin every setting that affects them:

from laiser.skill_extractor_refactored import SkillExtractorRefactored

extractor = SkillExtractorRefactored(
    model_id="Qwen/Qwen2.5-0.5B-Instruct",  # pin the model
    use_gpu=False,
    temperature=0.0,  # the default: greedy decoding
    seed=42,  # the default
)

results = extractor.extract_concepts(
    data=data,
    id_column="Research ID",
    text_columns=["description"],
    input_type="job_desc",
    concepts=["skills"],
    allowed_sources=["esco", "onet", "osn", "ukos"],
    similarity_thresholds={"skill": 0.60, "knowledge": 0.50, "task": 0.50},  # the defaults
)

Also record:

• the LAiSER version — it fixes the bundled taxonomy data and the embedding model used for alignment, sentence-transformers/all-MiniLM-L6-v2;
• the model revision, for Hugging Face models that may be updated under the same name.

What this does not guarantee

• Hosted models can change. A provider can update the model behind a name, and providers do not guarantee identical output even at temperature 0.0.
• Different models extract different phrases. Alignment can map different wordings of a skill onto the same taxonomy entry, but each phrase is matched on its own, so that is likely rather than guaranteed — and alignment cannot recover a skill a model never extracted. Compare models on the aligned columns, and measure their agreement rather than assuming it.
• Hardware and library versions matter for local models. Floating-point results can differ between devices and library releases.

How it is tested

tests/test_determinism.py runs in CI without a GPU, an API key or a language model download. The alignment checks use the real embedding model, which Hugging Face downloads on first use. It checks that:

• every public entry point defaults to temperature 0.0, and every seedable one to seed 42;
• the decoding parameters each backend actually receives match those defaults, and per-call overrides reach every backend;
• repeated alignment of the same phrases returns an identical table, similarity scores included.