Note:
langtuneis now part of the unifiedlangtrainSDK.pip install langtrain[train]includes everything from langtune plus AdaptiveRank, DatasetIntelligence, and vision LLM support. langtune continues to receive updates and remains fully supported.
The fastest way to fine-tune LLMs
Production-ready LoRA fine-tuning in minutes, not days.
Built for ML engineers who need results, not complexity.
Quick Start • Features • Why Langtune • Docs
⚡ Quick Start
1-Click Install (Recommended)
The fastest way to get started. Installs Langtune in an isolated environment.
curl -fsSL https://raw.githubusercontent.com/langtrain-ai/langtune/main/scripts/install.sh | bash
Or using pip
pip install langtune
Fine-tune your first model in 3 lines of code:
from langtune import LoRATrainer
trainer = LoRATrainer(model_name="meta-llama/Llama-2-7b-hf")
trainer.train_from_file("data.jsonl")
That's it. Your fine-tuned model is ready.
✨ Features
🎯 Why Langtune?
| Langtune | Others | |
|---|---|---|
| Time to first training | 30 seconds | 2+ hours |
| Lines of code | 3 | 100+ |
| Memory usage | 8GB | 24GB+ |
| Learning curve | Minutes | Days |
📖 Full Example
from langtune import LoRATrainer
from langtune.config import TrainingConfig, LoRAConfig
# Configure your training
config = TrainingConfig(
num_epochs=3,
batch_size=4,
learning_rate=2e-4,
lora=LoRAConfig(rank=16, alpha=32)
)
# Initialize and train
trainer = LoRATrainer(
model_name="mistralai/Mistral-7B-v0.1",
output_dir="./my-model",
config=config
)
# Train on your data
trainer.train_from_file("training_data.jsonl")
# Push to Hub (optional)
trainer.push_to_hub("my-username/my-fine-tuned-model")
🛠️ Advanced Usage
Custom Dataset Format
# JSONL format (recommended)
{"text": "Your training example here"}
{"text": "Another example"}
# Or instruction format
{"instruction": "Summarize this:", "input": "Long text...", "output": "Summary"}
Distributed Training
trainer = LoRATrainer(
model_name="meta-llama/Llama-2-70b-hf",
device_map="auto", # Automatic multi-GPU
)
Export Formats
# Export to different formats
trainer.export("gguf") # For llama.cpp
trainer.export("onnx") # For ONNX Runtime
trainer.export("hf") # HuggingFace format
🤝 Community
Built with ❤️ by Langtrain AI
Making LLM fine-tuning accessible to everyone.
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