lmdiff-kit 0.1.2

Framework for comparing language model configurations

lmdiff

Measures how and where two LLM configurations differ — not just whether one scores higher.

Compare language model configurations — not just weights, but weights + context + decoding + adapter + agent — via behavioral distance and multi-level diagnostics.

Why lmdiff?

lm-eval-harness tells you "model A scores 3 points higher than model B on MMLU." That's a scalar.

lmdiff tells you where those 3 points came from: which capabilities shifted, how far the output distribution moved, and whether two different modifications (e.g. a fine-tune vs. a context change) push behavior in the same direction or in opposite directions.

A Configuration is model + context + decoding + adapter + agent scaffold, not just model weights. Same checkpoint with a different system prompt is a different config — and lmdiff can quantify the difference.

Install

pip install lmdiff-kit

The import name is lmdiff; the PyPI distribution is lmdiff-kit (name disambiguation on PyPI).

Development install

mamba create -n lmdiff python=3.12
pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu130
pip install -e .

cu130 is for RTX 5090 / Blackwell. Pick the CUDA version that matches your GPU.

Command line

# Metric-level comparison (BD, token entropy, token KL)
lmdiff compare gpt2 distilgpt2 --probes v01

# Same, but JSON output to file
lmdiff compare gpt2 distilgpt2 --probes v01 --json --output result.json

# Per-domain capability radar (accuracy + BD per domain)
lmdiff radar gpt2 distilgpt2 --probes v01

# Single-model task evaluation
lmdiff run-task gpt2 --probes v01 --evaluator contains_answer

# List available metrics
lmdiff list-metrics

Python API

from lmdiff import Config, ModelDiff, ProbeSet
from lmdiff.report.terminal import print_report, print_radar

probes = ProbeSet.from_json("lmdiff/probes/v01.json")
md = ModelDiff(
    Config(model="gpt2"),
    Config(model="distilgpt2"),
    probes,
)

# Metric-level comparison
report = md.run(level="output", max_new_tokens=16)
print_report(report)

# Per-domain capability radar
radar_result = md.run_radar(probes=probes, max_new_tokens=16)
print_radar(radar_result)

Example: what lmdiff finds

Llama-2-7B vs YaRN-Llama-2-7b-128k on short prompts:

• BD = 1.03 nats — significant distributional shift even on prompts well within the original 4k context.
• TokenEntropy delta ≈ 0 — the distributions shifted direction, not spread; YaRN didn't make the model more or less uncertain on average.
• TokenKL = 0.35 — single-step distributions are similar, but multi-step generation diverges much further.
• Stopping behavior changed — YaRN learned to emit EOS after short answers; base Llama-2 keeps generating. Invisible to perplexity benchmarks; obvious in BD on generated continuations.

The point: same parameter count, similar single-step KL, but the generation behavior is meaningfully different — and the kind of difference is what lmdiff surfaces.

What gets measured

Three output-level metrics:

• BehavioralDistance — symmetric, self-entropy-baseline-subtracted cross-entropy distance. BPB-normalized when tokenizers differ.
• TokenEntropy — mean per-token next-token entropy delta, A vs B.
• TokenKL — symmetric KL divergence over full vocab.

CapabilityRadar adds per-domain accuracy + BD breakdown across math/knowledge/code (or any multi-domain probe set).

All return structured results with per-probe breakdowns in .details.

Configuration abstraction

A Config is more than a model name:

Config(
    model="gpt2",
    system_prompt="You are concise.",
    context=[{"role": "user", "content": "..."}],
    decode={"strategy": "sample", "temperature": 0.7},
    name="gpt2-concise",
)

Same weights + different context/decoding = different config = measurable behavioral difference.

JSON output

All results serialize to deterministic JSON with schema_version for forward compatibility:

from lmdiff.report.json_report import to_json, write_json
write_json(report, "output.json")

Status

Phase 1 shipped — published to PyPI as lmdiff-kit v0.1.0. Working: BehavioralDistance, TokenEntropy, TokenKL, CapabilityRadar, CLI, JSON reports, Python API.

Phase 2 in progress: Change Geometry — treating behavioral changes as vectors with direction/magnitude/cosine similarity across multiple variants.

Not yet: representation/trajectory/causal metrics, HTML/LaTeX reports, viz. See CLAUDE.md for the full roadmap.

Development

pytest                                    # fast tests (mocks only)
pytest -m slow -o "addopts="              # includes gpt2/distilgpt2 E2E

Architecture rules, implementation order, and coding conventions live in CLAUDE.md.

License

MIT — see LICENSE.

Citation

Paper forthcoming.