LFM2Sloth

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Quick Start

1. Setup environment with UV:

# Install UV if you haven't already
curl -LsSf https://astral.sh/uv/install.sh | sh

# Create virtual environment and install dependencies
uv sync

2. Run the quick start example:

cd examples
uv run python quick_start.py

This will train a small customer service model on sample data and test inference.

3. Try the trained model:

uv run python inference_demo.py output/quick_start/merged

Features

• Cross-Platform: CUDA (NVIDIA), Apple Silicon (MPS), and CPU support with automatic device detection
• Mathematical Reasoning: Advanced training for math problems with 60% GSM8K accuracy achievement
• Official Benchmarking: LM Evaluation Harness integration with format adapters for accurate evaluation
• Task Agnostic: Configure any training task through modular components
• Unsloth Optimized: 2-3x faster training with reduced memory usage
• Modular Design: Pluggable data, model, training, and evaluation components
• Experiment Tracking: Integrated W&B and TensorBoard support with user choice
• Easy Deployment: Model merging, quantization, and export utilities
• Multiple Formats: Support for ChatML, Alpaca, and custom data formats
• PDF Knowledge Extraction: Extract and train on technical documentation
• Dataset Integration: Seamless HuggingFace datasets integration

Architecture

src/talkytalky/
├── data/          # Data processing and format conversion
├── model/         # Model loading and configuration  
├── training/      # Training orchestration and experiment tracking
├── evaluation/    # Inference engine and evaluation metrics
└── deployment/    # Model export and deployment utilities

Usage Examples

Basic Training

# Train with default settings
uv run python train.py --train_data data/train.jsonl --eval_data data/val.jsonl

# Use a predefined configuration
uv run python train.py --train_data data/train.jsonl --config config/customer_service.json

# Quick training with 4-bit quantization
uv run python train.py --train_data data/train.jsonl --load_in_4bit --num_epochs 1

Data Conversion

from lfm2sloth.data import FormatConverter

# Convert CSV to training format
FormatConverter.csv_to_jsonl(
    csv_path="conversations.csv",
    output_path="training_data.jsonl", 
    user_column="customer_message",
    assistant_column="agent_response"
)

Custom Training Configuration

from lfm2sloth import ModelConfig, LoRAConfig, TrainingConfig, Trainer

# Configure model
model_config = ModelConfig(
    model_name="LiquidAI/LFM2-1.2B",
    max_seq_length=4096,
    load_in_4bit=True
)

# Configure LoRA
lora_config = LoRAConfig(r=16, lora_alpha=32)

# Configure training with experiment tracking
training_config = TrainingConfig(
    num_train_epochs=2,
    per_device_train_batch_size=2,
    learning_rate=2e-4,
    # Experiment tracking
    tracking_provider="tensorboard",  # "none", "wandb", "tensorboard", "both"
    experiment_name="my_experiment",
    project_name="my_project"
)

# Train
trainer = Trainer(model_config, training_config, lora_config)
trainer.prepare_model()
train_dataset, eval_dataset = trainer.prepare_datasets("train.jsonl", "val.jsonl")
trainer.train(train_dataset, eval_dataset)

Inference

from lfm2sloth.evaluation import InferenceEngine

# Load trained model
engine = InferenceEngine("path/to/model")

# Chat interface
response = engine.chat(
    user_message="I need help with my order",
    system_prompt="You are a customer support agent"
)

Experiment Tracking

LFM2Sloth provides integrated experiment tracking with support for both Weights & Biases and TensorBoard:

# TensorBoard tracking
training_config = TrainingConfig(
    tracking_provider="tensorboard",
    experiment_name="my_experiment",
    project_name="my_project",
    tracking_tags=["experiment", "v1"],
    tracking_notes="Testing new architecture",
    log_model_artifacts=True
)

# Weights & Biases tracking
training_config = TrainingConfig(
    tracking_provider="wandb",
    experiment_name="my_experiment", 
    project_name="my_project",
    wandb_api_key="your-api-key"  # or set WANDB_API_KEY env var
)

# Both simultaneously
training_config = TrainingConfig(
    tracking_provider="both"
)

View tracking results:

# TensorBoard
tensorboard --logdir runs/
# Then open http://localhost:6006

# W&B - visit https://wandb.ai/your-username/your-project

Configuration Files

Use JSON configuration files to define training tasks:

{
  "model": {
    "base_model": "LiquidAI/LFM2-1.2B",
    "max_seq_length": 4096
  },
  "lora": {
    "r": 16,
    "lora_alpha": 32,
    "lora_dropout": 0.05
  },
  "training": {
    "num_train_epochs": 2,
    "per_device_train_batch_size": 2,
    "learning_rate": 2e-4
  },
  "data": {
    "format": "chatml",
    "system_prompt": "You are a helpful assistant"
  }
}

See config/ directory for complete examples.

Deployment

from lfm2sloth.deployment import ModelDeployer

deployer = ModelDeployer("path/to/adapter")

# Merge LoRA with base model
deployer.merge_adapter("output/merged_model")

# Create quantized version
deployer.quantize_model("output/quantized_model", quantization_type="4bit")

# Export to ONNX
deployer.export_onnx("output/onnx_model")

# Create complete deployment package
deployer.create_deployment_package(
    "output/deployment_package",
    deployment_formats=["merged", "quantized"]
)

Command Line Interface

The main training script supports extensive configuration:

uv run python train.py \\
  --train_data data/train.jsonl \\
  --eval_data data/val.jsonl \\
  --model_name LiquidAI/LFM2-1.2B \\
  --output_dir output/my_model \\
  --num_epochs 2 \\
  --batch_size 2 \\
  --learning_rate 2e-4 \\
  --lora_r 16 \\
  --save_merged

See uv run python train.py --help for all options.

Examples & Demonstrations

Training Examples

• Mathematical Reasoning (examples/train_combined_math_expert.py): Train on GSM8K + Nemotron for 60% accuracy
• Cross-Platform Testing (examples/test_apple_silicon.py): Verify Apple Silicon (MPS) compatibility
• Official Benchmarking (examples/test_gsm8k_benchmark.py): LM Evaluation Harness integration
• PDF Knowledge Extraction (examples/build_pdf_dataset.py): Extract and train on technical documentation
• Conversational AI (examples/test_ultrachat_expert.py): Natural conversation using UltraChat dataset
• Experiment Tracking Demo (examples/experiment_tracking_demo.py): Complete tracking setup examples

Run Example Training

# Train mathematical reasoning model with combined datasets
uv run python examples/train_combined_math_expert.py

# Test Apple Silicon compatibility (M1/M2/M3 Macs)
uv run python examples/test_apple_silicon.py

# Evaluate with official GSM8K benchmarks
uv run python examples/test_gsm8k_benchmark.py

# Train PDF knowledge assistant from technical documentation
uv run python examples/test_pdf_assistant.py

# Train conversational model with UltraChat data  
uv run python examples/test_ultrachat_expert.py

Model Support

Currently optimized for:

• LiquidAI/LFM2-1.2B (primary target)
• Other Hugging Face compatible models (experimental)

Data Formats

ChatML Format (Recommended)

{"messages": [
  {"role": "system", "content": "You are a helpful assistant."},
  {"role": "user", "content": "Hello!"},
  {"role": "assistant", "content": "Hi! How can I help you?"}
]}

Alpaca Format

{
  "instruction": "Answer the user's question",
  "input": "What is the capital of France?",
  "output": "The capital of France is Paris."
}

Performance

LFM2Sloth leverages Unsloth optimizations for:

• 2-3x faster training compared to standard methods
• Reduced memory usage enabling larger batch sizes
• Efficient LoRA implementation for quick fine-tuning

Contributing

This is an open-source project built on top of Unsloth and Liquid AI's models. Contributions welcome!

License

This project is open source. Please respect the licensing terms of:

• Unsloth (Apache 2.0)
• LiquidAI models (LFM Open License v1.0)
• Hugging Face Transformers (Apache 2.0)

Acknowledgments

• Unsloth AI for the optimization framework
• Liquid AI for the LFM2 models
• Hugging Face for the transformers ecosystem