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LLaMA-Factory is an open-source tool that enables training and fine-tuning of open-source Large Language Models (LLMs) and Vision-Language Models (VLMs) without writing any code. With over 50,000 GitHub stars, this powerful platform supports:
- Popular models: LLaMA, Mistral, DeepSeek, Gemma, and more
- Efficient fine-tuning methods: LoRA, QLoRA, DoRA, LoRA+
- Integrated approaches: PPO, DPO, KTO, ORPO
- Performance optimizations: Flash Attention, RoPE scaling
- Experiment tracking: TensorBoard, W&B, MLflow
- Downstream tasks: Tool use, multimodal understanding
GitHub Repo: https://github.com/hiyouga/LLaMA-Factory
You Should Know: Practical Implementation Guide
1. Installation and Setup
Clone the repository git clone https://github.com/hiyouga/LLaMA-Factory.git cd LLaMA-Factory Create and activate a conda environment conda create -n llama-factory python=3.10 conda activate llama-factory Install dependencies pip install -r requirements.txt
2. Launching the Web UI
Start the web interface python src/train_web.py
3. Basic Fine-Tuning Command
Example fine-tuning command using LoRA CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ --stage sft \ --model_name_or_path meta-llama/Llama-2-7b-hf \ --do_train \ --dataset alpaca_gpt4_en \ --finetuning_type lora \ --lora_target q_proj,v_proj \ --output_dir output \ --per_device_train_batch_size 4 \ --gradient_accumulation_steps 4 \ --lr_scheduler_type cosine \ --logging_steps 10 \ --save_steps 1000 \ --learning_rate 5e-5 \ --num_train_epochs 3.0 \ --fp16
4. Monitoring Training
Launch TensorBoard to monitor training tensorboard --logdir output/runs
5. Model Inference
Run inference with your fine-tuned model python src/train_bash.py \ --stage sft \ --model_name_or_path output \ --do_predict \ --dataset alpaca_gpt4_en \ --checkpoint_dir output \ --output_dir output \ --predict_with_generate
6. Advanced Configuration
Multi-GPU training example torchrun --nproc_per_node=4 src/train_bash.py \ --stage sft \ --model_name_or_path meta-llama/Llama-2-70b-hf \ --do_train \ --dataset alpaca_gpt4_en \ --finetuning_type lora \ --output_dir output \ --per_device_train_batch_size 2 \ --gradient_accumulation_steps 8 \ --lr_scheduler_type cosine \ --logging_steps 10 \ --save_steps 1000 \ --learning_rate 1e-5 \ --num_train_epochs 2.0 \ --fp16
What Undercode Say
LLaMA-Factory represents a significant democratization of LLM fine-tuning, making advanced AI capabilities accessible to developers without requiring deep expertise in machine learning engineering. The tool’s comprehensive feature set addresses several critical aspects of modern LLM development:
- Efficiency: The support for parameter-efficient methods like LoRA and QLoRA reduces computational costs
- Flexibility: Multiple training approaches (PPO, DPO) enable different optimization strategies
- Scalability: The ability to handle large models like LLaMA-2-70B demonstrates production readiness
- Observability: Integration with tools like TensorBoard and Weights & Biases ensures proper experiment tracking
For developers looking to implement custom LLM solutions, consider these additional commands for system optimization:
Monitor GPU usage during training
nvidia-smi -l 1
Clean up PyTorch cache if you encounter memory issues
rm -rf ~/.cache/torch
Benchmark inference speed
python -c "from transformers import AutoModelForCausalLM; import time; \
model = AutoModelForCausalLM.from_pretrained('output'); \
start = time.time(); outputs = model.generate(input_ids, max_length=50); \
print(f'Generated in {time.time()-start:.2f}s')"
Expected Output:
Training started with configuration: - Model: meta-llama/Llama-2-7b-hf - Dataset: alpaca_gpt4_en - Fine-tuning type: lora - Batch size: 4 - Learning rate: 5e-5 - Epochs: 3.0 Epoch 1/3: 100%|██████████| 1000/1000 [10:20<00:00, 1.62it/s, loss=1.23] Epoch 2/3: 100%|██████████| 1000/1000 [09:45<00:00, 1.70it/s, loss=0.89] Epoch 3/3: 100%|██████████| 1000/1000 [09:30<00:00, 1.75it/s, loss=0.67] Model saved to output/ Inference test completed with 98.7% accuracy
References:
Reported By: Avi Chawla – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
