axolotl-rs

YAML-driven configurable fine-tuning toolkit for LLMs in Rust.

Overview

axolotl-rs is a Rust port of the Python Axolotl project, providing a framework for fine-tuning language models.

Features:

• YAML Configuration - Parse and validate training configuration files
• Dataset Handling - Load datasets in Alpaca, ShareGPT, completion, and custom formats
• CLI Interface - Commands for validate, train, merge, init
• Configuration Presets - Templates for LLaMA-2, Mistral, and Phi-3 models
• Adapter Integration - LoRA and QLoRA via peft-rs and qlora-rs
• Training Loop - Forward/backward passes with checkpoint support

Installation

# From crates.io
cargo install axolotl-rs

# Or from source
git clone https://github.com/tzervas/axolotl-rs
cd axolotl-rs
cargo build --release

Quick Start

1. Generate a Configuration

# Create a config for LLaMA-2 7B with QLoRA
axolotl init config.yaml --preset llama2-7b

2. Prepare Your Dataset

Create a JSONL file in Alpaca format:

{"instruction": "Explain quantum computing", "input": "", "output": "Quantum computing uses..."}
{"instruction": "Write a haiku about Rust", "input": "", "output": "Memory safe code\n..."}

3. Validate Configuration

axolotl validate config.yaml

4. Start Training

axolotl train config.yaml

5. Merge Adapters (Optional)

axolotl merge --config config.yaml --output ./merged-model

Configuration

Full Example

# config.yaml
base_model: meta-llama/Llama-2-7b-hf
adapter: qlora

# LoRA settings
lora:
  r: 64
  alpha: 16
  dropout: 0.05
  target_modules:
    - q_proj
    - k_proj
    - v_proj
    - o_proj

# Quantization (for QLoRA)
quantization:
  bits: 4
  quant_type: nf4
  double_quant: true

# Dataset
dataset:
  path: ./data/train.jsonl
  format: alpaca
  max_length: 2048
  val_split: 0.05

# Training
training:
  epochs: 3
  batch_size: 4
  gradient_accumulation_steps: 4
  learning_rate: 2.0e-4
  lr_scheduler: cosine
  warmup_ratio: 0.03
  save_steps: 500
  gradient_checkpointing: true

output_dir: ./outputs/my-model
seed: 42

Dataset Formats

Available Presets

• llama2-7b - LLaMA-2 7B with QLoRA
• mistral-7b - Mistral 7B with QLoRA
• phi3-mini - Phi-3 Mini with LoRA

CLI Commands

# Validate configuration
axolotl validate <config.yaml>

# Start training
axolotl train <config.yaml>
axolotl train <config.yaml> --resume ./checkpoint-1000

# Merge adapter into base model
axolotl merge --config <config.yaml> --output <path>

# Generate sample config
axolotl init <output.yaml> --preset <preset>

Architecture

axolotl-rs
├── config     - YAML parsing & validation
├── dataset    - Data loading & preprocessing
├── model      - Model loading & adapter management
└── trainer    - Training loop & optimization

Dependencies:
├── candle-*   - Tensor operations and transformer models
├── tokenizers - HuggingFace tokenizer bindings
├── peft-rs    - LoRA/DoRA adapter support (optional)
├── qlora-rs   - 4-bit quantization (optional)
└── unsloth-rs - Optimized kernels (optional)

Feature Flags

Documentation

• ARCHITECTURE.md - Technical architecture details
• CONTRIBUTING.md - Contribution guidelines
• TEST_COVERAGE_PLAN.md - Test coverage goals

Porting from Python: This is a Rust port of the Python Axolotl project, designed for better performance and efficiency.

Contributing

Contributions welcome! See CONTRIBUTING.md for guidelines.

License

Licensed under the MIT License.