finetunecheck 0.1.4

Automated base vs fine-tuned LLM comparison with forgetting detection, capability retention scoring, and visual diff reports.

FineTuneCheck

Diagnostic tool for LLM fine-tuning outcomes.

Automated base-vs-fine-tuned comparison with forgetting detection, capability retention scoring, and visual diff reports.

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The Problem

You fine-tuned a model. It's better at your task. But what did it forget?

Fine-tuning improves target capabilities at the cost of general ones. Without measurement, you're shipping blind — did safety degrade? Is reasoning still intact? Was the trade-off worth it?

FineTuneCheck answers these questions in one command.

Features

• 12 built-in probe categories — reasoning, code, math, safety, chat, creative writing, and more
• 4 forgetting metrics — Backward Transfer, Capability Retention Rate, Selective Forgetting Index, Safety Alignment Retention
• Multi-judge system — exact match, F1, rule-based, ROUGE, LLM-as-judge
• Deep analysis — CKA, spectral, perplexity shift, calibration (ECE), activation drift
• Multi-run comparison — Pareto frontier across fine-tuning runs
• 5 verdict levels — EXCELLENT → GOOD → GOOD_WITH_CONCERNS → POOR → HARMFUL
• Composite ROI score — 0-100 balancing improvement vs forgetting cost
• HTML/JSON/CSV/Markdown reports — interactive Plotly charts
• MCP server — 9 tools for AI assistant integration
• LoRA + GGUF support — works with PEFT adapters and quantized models

Install

pip install finetunecheck

Optional backends:

pip install finetunecheck[api-judge]   # LLM-as-judge (Anthropic + OpenAI)
pip install finetunecheck[vllm]        # vLLM inference backend
pip install finetunecheck[gguf]        # GGUF model support
pip install finetunecheck[mcp]         # MCP server for AI assistants
pip install finetunecheck[all]         # Everything

Quick Start

CLI

Run a full evaluation against the base model:

ftcheck run meta-llama/Llama-3-8B ./my-finetuned-model \
  --profile code --report report.html

Quick 5-minute sanity check (20 samples, 4 categories):

ftcheck quick meta-llama/Llama-3-8B ./my-finetuned-model

Compare multiple fine-tuning runs with Pareto frontier analysis:

ftcheck compare meta-llama/Llama-3-8B ./run1 ./run2 ./run3 \
  --report comparison.html

Deep analysis — CKA, spectral, perplexity shift, calibration:

ftcheck run meta-llama/Llama-3-8B ./my-finetuned-model --deep

Browse available probes and profiles:

ftcheck list-probes
ftcheck list-profiles

Python API

from finetunecheck import EvalRunner
from finetunecheck.config import EvalConfig

config = EvalConfig(
    base_model="meta-llama/Llama-3-8B",
    finetuned_model="./my-finetuned-model",
    profile="code",
    deep_analysis=True,
)

runner = EvalRunner(config)
results = runner.run()

print(f"Verdict: {results.verdict.value}")        # GOOD_WITH_CONCERNS
print(f"ROI Score: {results.roi_score}")           # 72.5
print(f"BWT: {results.forgetting.backward_transfer:+.3f}")  # -0.082
print(f"Safety: {results.forgetting.safety_alignment_retention}")  # 0.97

Probe Categories

Category	Samples	Judge	What It Tests
reasoning	15 (seed set)	LLM	Logical deduction, chain-of-thought
code	15 (seed set)	rule-based	Code generation, debugging
math	15 (seed set)	exact match	Arithmetic, algebra, word problems
safety	10 (seed set)	rule-based	Refusal of harmful prompts, alignment
chat_quality	10 (seed set)	LLM	Helpfulness, coherence, tone
creative_writing	8 (seed set)	LLM	Storytelling, style, creativity
summarization	10 (seed set)	ROUGE	Compression, faithfulness
extraction	10 (seed set)	F1	Named entities, structured data
classification	12 (seed set)	exact match	Sentiment, topic, intent
instruction_following	12 (seed set)	rule-based	Format compliance, constraints
multilingual	10 (seed set)	LLM	Translation, cross-lingual transfer
world_knowledge	15 (seed set)	exact match	Facts, trivia, common sense

Forgetting Metrics

Metric	Formula	Interpretation
BWT (Backward Transfer)	avg(ft − base) on non-target categories	Negative = forgetting
CRR (Capability Retention Rate)	ft_score / base_score per category	< 0.95 = meaningful regression
SFI (Selective Forgetting Index)	std(CRR values)	High = uneven forgetting
SAR (Safety Alignment Retention)	ft_safety / base_safety	< 0.90 → HARMFUL verdict

Verdict System

Verdict	ROI Score	Meaning
EXCELLENT	85-100	Strong improvement, minimal forgetting
GOOD	70-84	Solid improvement, acceptable trade-offs
GOOD_WITH_CONCERNS	50-69	Improvement exists but forgetting is notable
POOR	25-49	Marginal improvement, significant forgetting
HARMFUL	0-24	Safety degraded or catastrophic forgetting

Deep Analysis

Enable with --deep for additional diagnostics:

• CKA Similarity — per-layer representation alignment between base and fine-tuned
• Spectral Analysis — effective rank changes, singular value distribution
• Perplexity Distribution Shift — KL divergence and Wasserstein distance of per-token perplexity
• Calibration (ECE) — expected calibration error before and after fine-tuning
• Activation Drift — per-layer cosine similarity, disrupted attention heads

Multi-Run Comparison

ftcheck compare base_model ./run1 ./run2 ./run3 --report comparison.html

Outputs per-run verdicts, best overall / best target / least forgetting picks, and Pareto frontier analysis.

Custom Probes

from finetunecheck.probes.registry import ProbeRegistry

ProbeRegistry.register_from_csv("my_probes.csv", name="custom", category="domain")
ProbeRegistry.register_from_jsonl("my_probes.jsonl", name="custom", category="domain")

Evaluation Profiles

Profile	Focus Areas
general	Balanced evaluation across all capability categories
code	Code generation, mathematical reasoning
chat	Chat quality, instruction following, multilingual, safety
classification	Classification, extraction (lightweight)
rag	Extraction, summarization, factual knowledge
medical	Reasoning, factual accuracy, safety (medical domain)
legal	Reasoning, extraction (legal domain)
safety_critical	All categories with extreme safety weight (99%+ SAR)

MCP Integration

{
  "mcpServers": {
    "finetunecheck": {
      "command": "ftcheck",
      "args": ["serve", "--stdio"]
    }
  }
}

Tools: run_evaluation, quick_check, compare_runs, get_forgetting_report, list_probes, list_profiles, get_probe_details, analyze_deep, generate_report

Export Formats

ftcheck run base ft --report results.html -f html       # Interactive HTML
ftcheck run base ft --report results.json -f json       # Machine-readable
ftcheck run base ft --report results.csv -f csv         # Spreadsheet
ftcheck run base ft --report results.md -f markdown     # Documentation

CI Integration

# Exit code 1 if verdict is POOR or HARMFUL
ftcheck run base_model finetuned_model --exit-code

Architecture

finetunecheck/
├── eval/           # EvalRunner pipeline, judges, scoring
├── forgetting/     # BWT, CRR, SFI, SAR metrics
├── compare/        # Multi-run comparison, Pareto frontier
├── deep_analysis/  # CKA, spectral, perplexity, calibration
├── probes/         # 12 built-in probe sets + custom probe support
├── report/         # HTML/JSON/CSV/Markdown generation
├── mcp/            # MCP server (9 tools)
└── models.py       # Pydantic v2 data contracts

Development

git clone https://github.com/shuhulx/finetunecheck.git
cd finetunecheck
pip install -e ".[dev]"
pytest

Contributions welcome! Please open an issue first to discuss proposed changes.

References

• Luo et al., "An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning" (2023)
• Kornblith et al., "Similarity of Neural Network Representations Revisited" (ICML 2019) — CKA
• Guo et al., "On Calibration of Modern Neural Networks" (2017) — ECE

License

Apache 2.0