Internal Coherence Maximization (ICM)

ICM (Internal Coherence Maximization) is a Python tool for unsupervised elicitation of language models. Based on the paper "Unsupervised Elicitation of Language Models", ICM fine-tunes pretrained language models on their own generated labels without external supervision.

• Unsupervised Learning: Generate high-quality labeled datasets without human supervision
• Mutual Predictability: Find labels that are logically consistent and mutually predictable
• Multiple Task Types: Support for classification, comparison, mathematical reasoning, and more
• Flexible Export: Export to various formats (DPO, CSV, JSON) and push to Hugging Face

git clone https://github.com/codelion/icm.git
cd icm
pip install -e .

pip install -r requirements.txt

Generate a labeled dataset using ICM:

icm run --model google/gemma-3-1b-it --dataset truthful_qa --task-type truthfulqa --max-examples 100

icm export --input-path icm_results/truthfulqa_dialoGPT_20240115_143022.jsonl --output-path truthfulqa_dpo.jsonl --format dpo

icm push --input-path truthfulqa_dpo.jsonl --hf-repo-id your-username/icm-truthfulqa-dataset

Use Case	Dataset	Link
Fine-tuning the model	dpo dataset

ICM uses two key components:

1. Mutual Predictability: Measures how well the model can predict each label given all other labels
2. Logical Consistency: Enforces simple logical constraints to prevent degenerate solutions

The algorithm uses simulated annealing to search for optimal label assignments that maximize:

U(D) = α × P_θ(D) - I(D)

Where:

• P_θ(D) is the mutual predictability score
• I(D) is the inconsistency penalty
• α balances the two terms

# Fully automatic - detects config='multiple_choice' and split='validation'
icm run --model google/gemma-3-1b-it --dataset truthful_qa --task-type truthfulqa

# Or explicitly specify parameters
icm run --model google/gemma-3-1b-it --dataset truthful_qa --config multiple_choice --split validation --task-type truthfulqa

# Fully automatic - detects config='main'
icm run --model google/gemma-3-1b-it --dataset gsm8k --task-type gsm8k

# Or explicitly specify parameters
icm run --model google/gemma-3-1b-it --dataset gsm8k --config main --task-type gsm8k

icm run --model google/gemma-3-1b-it --dataset path/to/dataset.jsonl --task-type classification

ICM can generate synthetic datasets for testing and experimentation. These are perfect for:

• Testing ICM: Validate the algorithm on simple, verifiable tasks
• Quick experiments: Generate datasets instantly without external dependencies
• Educational purposes: Understand how ICM works with clear logical relationships

Generates simple addition problems with both correct and incorrect solutions:

Example Output:

Question: What is 42 + 17?
Claim: 42 + 17 = 59
I think this Claim is [True/False]

How it works:

• Random numbers between 1-100
• Creates correct solutions (True labels)
• Creates incorrect solutions with random errors (False labels)
• Double the requested size: --synthetic-size 500 creates 1000 examples (500 correct + 500 incorrect)
• Perfectly balanced: 50% True, 50% False labels

Generates number comparison tasks:

Example Output:

Query: Which number is larger?
Response A: 73
Response B: 45
Claim: Response A is larger than Response B
I think this Claim is [True/False]

How it works:

• Random pairs of numbers
• True/False based on actual comparison
• Single example per iteration (not doubled)

# Math problems - creates 1000 examples (500 pairs)
icm run --model google/gemma-3-1b-it --synthetic math --synthetic-size 500

# Number comparisons - creates 300 examples  
icm run --model google/gemma-3-1b-it --synthetic comparison --synthetic-size 300

# Quick test with defaults (100 examples)
icm run --model google/gemma-3-1b-it --synthetic math

1. Instant generation: No need to download or configure external datasets
2. Verifiable ground truth: Clear logical relationships for validation
3. Reproducible: Consistent results with same seed
4. Perfect for testing: Simple tasks ideal for algorithm validation
5. No dependencies: Works offline without internet connection

All synthetic examples follow the standard ICM format:

{
  "input": "Question: What is 42 + 17?\nClaim: 42 + 17 = 59\nI think this Claim is [True/False]",
  "metadata": {
    "gold_label": "True",
    "task": "math"
  }
}

Run ICM on a dataset to generate labeled examples.

Required Arguments:

• --model: Model name or path (e.g., google/gemma-3-1b-it)

Dataset Arguments:

• --dataset: Dataset name or path
• --task-type: Task type (auto, classification, comparison, truthfulqa, gsm8k)
• --split: Dataset split (default: train)
• --max-examples: Maximum examples to process

Synthetic Dataset Options:

• --synthetic: Create synthetic dataset (math, comparison)
• --synthetic-size: Number of synthetic examples to generate (default: 100)

ICM Algorithm Parameters:

• --alpha: Weight for mutual predictability vs consistency (default: 100.0)
• --initial-temperature: Starting temperature for simulated annealing (default: 3.0)
• --final-temperature: Ending temperature (default: 0.001)
• --cooling-rate: Temperature cooling rate (default: 0.98)
• --initial-examples: Number of initial random examples (default: 20)
• --max-iterations: Maximum search iterations (default: 1000)

Generation Parameters:

• --generation-temperature: Temperature for text generation (default: 0.2)
• --generation-top-p: Top-p for nucleus sampling (default: 0.9)
• --generation-max-tokens: Maximum tokens to generate (default: 512)

System Parameters:

• --device: Computation device (cuda, cpu, auto)
• --seed: Random seed for reproducibility (default: 42)
• --log-level: Logging level (DEBUG, INFO, WARNING, ERROR)

Export ICM results to various formats.

Required Arguments:

• --input-path: Path to ICM result file
• --output-path: Output file path
• --format: Export format (json, dpo, csv, analysis)

Optional Arguments:

• --include-stats: Include statistics in JSON export
• --create-pairs: Create chosen/rejected pairs for DPO format
• --hf-push: Push to Hugging Face after export
• --hf-repo-id: Hugging Face repository ID
• --private: Make Hugging Face repository private

Push files to Hugging Face Hub.

Required Arguments:

• --input-path: Local file path to upload
• --hf-repo-id: Hugging Face repository ID (e.g., username/dataset-name)

Optional Arguments:

• --file-name: Custom filename in repository
• --private: Make repository private

List all saved ICM results.

icm list --results-dir icm_results

Analyze ICM results and show statistics.

# Analyze all results
icm analyze

# Analyze specific result file
icm analyze --result-file icm_results/truthfulqa_gpt2_20240115_143022.jsonl

Clean old result files, keeping only the latest N results.

icm clean --keep-latest 10

Create a config.json file:

{
  "search_params": {
    "alpha": 30.0,
    "initial_temperature": 15.0,
    "final_temperature": 0.005,
    "max_iterations": 2000
  },
  "model_params": {
    "generation_temperature": 0.8,
    "generation_top_p": 0.95
  },
  "system_params": {
    "device": "cuda",
    "seed": 123
  }
}

Set common parameters via environment variables:

export ICM_MODEL="google/gemma-3-1b-it"
export ICM_DEVICE="cuda"
export ICM_LOG_LEVEL="INFO"

from icm import ICMSearcher, load_icm_dataset

# Load dataset
dataset = load_icm_dataset("truthful_qa", task_type="truthfulqa")

# Create searcher
searcher = ICMSearcher(
    model_name="google/gemma-3-1b-it",
    alpha=50.0,
    max_iterations=1000
)

# Run ICM search
result = searcher.search(dataset, max_examples=100)

# Access results
print(f"Generated {len(result.labeled_examples)} labeled examples")
print(f"Final score: {result.score:.4f}")

from icm import ICMSearcher, ICMDataset, ICMExample
from icm.consistency import LogicalConsistencyChecker, MathConsistencyRule

# Create custom dataset
examples = [
    ICMExample("What is 2+2?", {"category": "math"}),
    ICMExample("What is 3+3?", {"category": "math"})
]
dataset = ICMDataset(examples)

# Custom consistency checker
checker = LogicalConsistencyChecker([MathConsistencyRule()])

# Advanced searcher
searcher = ICMSearcher(
    model_name="google/gemma-3-1b-it",
    alpha=30.0,
    initial_temperature=20.0,
    consistency_checker=checker,
    seed=42
)

result = searcher.search(dataset)

from icm.storage import ICMStorage
from icm.exporters import ICMExporter

# Save results
storage = ICMStorage("my_results")
storage.save_result(result, "experiment_1")

# Export to DPO format
exporter = ICMExporter(storage)
exporter.export_to_dpo_format(
    result.labeled_examples,
    "training_data.jsonl"
)

# Push to Hugging Face
exporter.export_to_huggingface(
    result.labeled_examples,
    repo_id="username/my-icm-dataset",
    task_type="classification",
    model_name="google/gemma-3-1b-it"
)

# Create synthetic math dataset
icm run --model google/gemma-3-1b-it --synthetic math --synthetic-size 500 --max-iterations 500

# Use real GSM8K dataset  
icm run --model google/gemma-3-1b-it --dataset gsm8k --task-type gsm8k --max-examples 200

# Generate preference dataset
icm run --model google/gemma-3-1b-it --dataset anthropic/hh-rlhf --task-type comparison --alpha 30.0

# Export to DPO format for training
icm export --input-path results.jsonl --output-path dpo_data.jsonl --format dpo --create-pairs

# Export analysis report
icm export --input-path results.jsonl --output-path analysis.json --format analysis --include-examples

CUDA Out of Memory:

# Use smaller model, MPS (Apple Silicon), or CPU
icm run --model google/gemma-3-1b-it --device cpu
# or on Apple Silicon:
icm run --model google/gemma-3-1b-it --device mps

Model Loading Errors:

# Verify model name and check internet connection
icm run --model google/gemma-3-1b-it --log-level DEBUG

Poor Quality Results:

# Increase alpha or iterations
icm run --model your-model --alpha 100.0 --max-iterations 2000

Dataset Configuration Errors:

# ICM now auto-detects both config and split for known datasets
# TruthfulQA: automatically uses config='multiple_choice' and split='validation'
# GSM8K: automatically uses config='main' and split='train'

# Your commands should work automatically:
icm run --model google/gemma-3-1b-it --dataset truthful_qa --task-type truthfulqa
icm run --model google/gemma-3-1b-it --dataset gsm8k --task-type gsm8k

# Or specify manually if needed:
icm run --model google/gemma-3-1b-it --dataset truthful_qa --config multiple_choice --split validation --task-type truthfulqa
icm run --model google/gemma-3-1b-it --dataset gsm8k --config main --task-type gsm8k

Memory Usage Issues:

# ICM uses memory-efficient sampling to handle large datasets
# If you still encounter memory issues, reduce the dataset size:
icm run --model google/gemma-3-1b-it --dataset large-dataset --max-examples 50

# Or use a smaller model:
icm run --model distilgpt2 --dataset your-dataset --max-examples 100

Enable detailed logging:

icm run --model google/gemma-3-1b-it --dataset your-data --log-level DEBUG --log-file debug.log

git clone https://github.com/codelion/icm.git
cd icm
pip install -e ".[dev]"

pytest tests/

If you use ICM in your research, please cite:

@software{icm,
  title = {ICM: Internal Coherence Maximization},
  author = {Asankhaya Sharma},
  year = {2025},
  publisher = {GitHub},
  url = {https://github.com/codelion/icm}
}

• Eliciting Fine-Tuned Transformer Capabilities: Paper
• Weak-to-Strong Generalization: Paper
• Constitutional AI: Paper
• Discovering Latent Knowledge: Paper