Unsloth Fine-Tuning

Expert guidance for fine-tuning Large Language Models using Unsloth's optimized library.

Core Capabilities

• Load models with 4-bit quantization and gradient checkpointing
• Configure LoRA/QLoRA for efficient fine-tuning
• Train on custom or Hugging Face datasets
• Export models to GGUF, Ollama, vLLM, or Hugging Face formats
• Monitor training with progress tracking
• Optimize for different hardware configurations

Quick Start

1. Load a Model

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="unsloth/Llama-3.2-1B-bnb-4bit",
    max_seq_length=2048,
    load_in_4bit=True,
    use_gradient_checkpointing="unsloth"
)

Supported Models:

• Llama 3.3 (70B), 3.2 (1B, 3B), 3.1 (8B)
• Mistral v0.3 (7B), Small Instruct
• Phi 3.5 mini, Phi 3 medium
• Gemma 2 (9B, 27B)
• Qwen 2.5 (7B)

2. Apply LoRA

model = FastLanguageModel.get_peft_model(
    model,
    r=16,                    # LoRA rank (8-64)
    target_modules=["q_proj", "k_proj", "v_proj", "o_proj",
                    "gate_proj", "up_proj", "down_proj"],
    lora_alpha=16,          # Scaling factor
    use_gradient_checkpointing="unsloth",
    random_state=3407,
    max_seq_length=2048
)

3. Configure Training

from trl import SFTTrainer, SFTConfig

trainer = SFTTrainer(
    model=model,
    train_dataset=dataset["train"],
    tokenizer=tokenizer,
    args=SFTConfig(
        per_device_train_batch_size=2,
        gradient_accumulation_steps=4,
        warmup_steps=10,
        max_steps=100,
        learning_rate=2e-4,
        logging_steps=1,
        output_dir="./output",
        optim="adamw_8bit",
        seed=3407
    )
)

4. Train

trainer.train()

5. Export

# GGUF format
model.save_pretrained_gguf(
    "model",
    tokenizer,
    quantization_method="q4_k_m"
)

# Hugging Face format
model.save_pretrained("./hf_model")
tokenizer.save_pretrained("./hf_model")

Performance Optimization

Memory Optimization

Out of Memory? Try:

1. Reduce per_device_train_batch_size to 1
2. Increase gradient_accumulation_steps to 8
3. Reduce max_seq_length to 1024
4. Use smaller model (1B instead of 3B)

Speed Optimization

Training too slow? Check:

1. GPU is being used: nvidia-smi
2. Batch size isn't too small
3. Using load_in_4bit=True
4. Using use_gradient_checkpointing="unsloth"

Quality Optimization

Poor results? Adjust:

1. Increase max_steps to 500-1000
2. Try learning rates: 1e-4, 2e-4, 5e-4
3. Increase dataset quality/size
4. Use larger model if resources allow

Hardware Requirements

Minimum (1B models)

• GPU: RTX 3060 (12GB VRAM)
• RAM: 16GB
• Training time: 20-40 min for 100 steps

Recommended (3B-7B models)

• GPU: RTX 4090 or A100
• RAM: 32GB+
• Training time: 10-30 min for 100 steps

Budget (Small experiments)

• GPU: RTX 3060 Ti (8GB)
• Use: Llama-1B or Phi-3-mini
• Reduce batch_size=1, max_seq_length=1024

Common Patterns

Pattern 1: Quick Prototype

# Minimal setup for fast experimentation
model, tokenizer = FastLanguageModel.from_pretrained(
    "unsloth/Llama-3.2-1B-bnb-4bit",
    max_seq_length=1024,    # Shorter for speed
    load_in_4bit=True
)

model = FastLanguageModel.get_peft_model(model, r=8)  # Lower rank

trainer = SFTTrainer(
    model=model,
    train_dataset=dataset["train"],
    tokenizer=tokenizer,
    args=SFTConfig(
        per_device_train_batch_size=2,
        max_steps=50,        # Few steps
        learning_rate=2e-4,
        output_dir="./quick_test"
    )
)

Pattern 2: Production Quality

# Full setup for best results
model, tokenizer = FastLanguageModel.from_pretrained(
    "unsloth/Llama-3.1-8B-bnb-4bit",
    max_seq_length=2048,
    load_in_4bit=True,
    use_gradient_checkpointing="unsloth"
)

model = FastLanguageModel.get_peft_model(
    model,
    r=16,                   # Standard rank
    lora_alpha=16,
    use_gradient_checkpointing="unsloth"
)

trainer = SFTTrainer(
    model=model,
    train_dataset=dataset["train"],
    tokenizer=tokenizer,
    args=SFTConfig(
        per_device_train_batch_size=2,
        gradient_accumulation_steps=4,
        max_steps=500,       # More steps
        learning_rate=2e-4,
        warmup_steps=10,
        logging_steps=10,
        save_steps=100,
        output_dir="./production_model"
    )
)

Pattern 3: Large Model (70B)

# Special settings for very large models
model, tokenizer = FastLanguageModel.from_pretrained(
    "unsloth/Llama-3.3-70B-Instruct-bnb-4bit",
    max_seq_length=2048,
    load_in_4bit=True,
    use_gradient_checkpointing="unsloth"
)

model = FastLanguageModel.get_peft_model(
    model,
    r=16,
    target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],  # Fewer targets
    use_gradient_checkpointing="unsloth"
)

trainer = SFTTrainer(
    model=model,
    train_dataset=dataset["train"],
    tokenizer=tokenizer,
    args=SFTConfig(
        per_device_train_batch_size=1,    # Must be 1
        gradient_accumulation_steps=8,    # Compensate
        max_steps=200,
        learning_rate=1e-4,               # Lower LR
        output_dir="./large_model"
    )
)

Troubleshooting

Error: "CUDA out of memory"

Solution:

# Reduce memory usage
batch_size = 1
max_seq_length = 1024
gradient_accumulation_steps = 8
# Or use smaller model

Error: "Model not found"

Solution:

• Check model name spelling
• Verify internet connection
• Try with Hugging Face token: export HF_TOKEN=your_token

Error: "Training loss not decreasing"

Solution:

# Adjust hyperparameters
learning_rate = 5e-4  # Try higher
max_steps = 500       # Train longer
# Or check dataset quality

Best Practices

1. Always use 4-bit quantization unless you have >80GB VRAM
2. Start with small models (1B) for experimentation
3. Monitor GPU usage with nvidia-smi
4. Save checkpoints every 100 steps
5. Validate on test set before exporting
6. Use appropriate LoRA rank: 8 for experiments, 16 for production, 32 for complex tasks

Dataset Format

Unsloth works with Hugging Face datasets. Example format:

{
  "text": "### Instruction: Explain quantum computing\n### Response: Quantum computing uses quantum bits..."
}

Or instruction format:

{
  "instruction": "Explain quantum computing",
  "input": "",
  "output": "Quantum computing uses quantum bits..."
}

Performance Benchmarks

Additional Resources

For more advanced topics, see:

• ADVANCED.md - Multi-GPU, custom optimizers, advanced LoRA
• DATASETS.md - Dataset preparation and formatting
• EXPORT.md - Detailed export options and formats

Version Compatibility

• Python: 3.10, 3.11, 3.12 (not 3.13)
• CUDA: 11.8 or 12.1+
• PyTorch: 2.0+
• Transformers: 4.37+