Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.
apm install @orchestra-research/peft-fine-tuning---
name: peft-fine-tuning
description: Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Fine-Tuning, PEFT, LoRA, QLoRA, Parameter-Efficient, Adapters, Low-Rank, Memory Optimization, Multi-Adapter]
dependencies: [peft>=0.13.0, transformers>=4.45.0, torch>=2.0.0, bitsandbytes>=0.43.0]
---
# PEFT (Parameter-Efficient Fine-Tuning)
Fine-tune LLMs by training <1% of parameters using LoRA, QLoRA, and 25+ adapter methods.
## When to use PEFT
**Use PEFT/LoRA when:**
- Fine-tuning 7B-70B models on consumer GPUs (RTX 4090, A100)
- Need to train <1% parameters (6MB adapters vs 14GB full model)
- Want fast iteration with multiple task-specific adapters
- Deploying multiple fine-tuned variants from one base model
**Use QLoRA (PEFT + quantization) when:**
- Fine-tuning 70B models on single 24GB GPU
- Memory is the primary constraint
- Can accept ~5% quality trade-off vs full fine-tuning
**Use full fine-tuning instead when:**
- Training small models (<1B parameters)
- Need maximum quality and have compute budget
- Significant domain shift requires updating all weights
## Quick start
### Installation
```bash
# Basic installation
pip install peft
# With quantization support (recommended)
pip install peft bitsandbytes
# Full stack
pip install peft transformers accelerate bitsandbytes datasets
```
### LoRA fine-tuning (standard)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer
from peft import get_peft_model, LoraConfig, TaskType
from datasets import load_dataset
# Load base model
model_name = "meta-llama/Llama-3.1-8B"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)
tokenizer.pad_token = tokenizer.eos_token
# LoRA configuration
lora_config = LoraConfig(
task_type=TaskType.CAUSAL_LM,
r=16, # Rank (8-64, higher = more capacity)
lora_alpha=32, # Scaling factor (typically 2*r)
lora_dropout=0.05, # Dropout for regularization
target_modules=["q_proj", "v_proj", "k_proj", "o_proj"], # Attention layers
bias="none" # Don't train biases
)
# Apply LoRA
model = get_peft_model(model, lora_config)
model.print_trainable_parameters()
# Output: trainable params: 13,631,488 || all params: 8,043,307,008 || trainable%: 0.17%
# Prepare dataset
dataset = load_dataset("databricks/databricks-dolly-15k", split="train")
def tokenize(example):
text = f"### Instruction:\n{example['instruction']}\n\n### Response:\n{example['response']}"
return tokenizer(text, truncation=True, max_length=512, padding="max_length")
tokenized = dataset.map(tokenize, remove_columns=dataset.column_names)
# Training
training_args = TrainingArguments(
output_dir="./lora-llama",
num_train_epochs=3,
per_device_train_batch_size=4,
gradient_accumulation_steps=4,
learning_rate=2e-4,
fp16=True,
logging_steps=10,
save_strategy="epoch"
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized,
data_collator=lambda data: {"input_ids": torch.stack([f["input_ids"] for f in data]),
"attention_mask": torch.stack([f["attention_mask"] for f in data]),
"labels": torch.stack([f["input_ids"] for f in data])}
)
trainer.train()
# Save adapter only (6MB vs 16GB)
model.save_pretrained("./lora-llama-adapter")
```
### QLoRA fine-tuning (memory-efficient)
```python
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
from peft import get_peft_model, LoraConfig, prepare_model_for_kbit_training
# 4-bit quantization config
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4", # NormalFloat4 (best for LLMs)
bnb_4bit_compute_dtype="bfloat16", # Compute in bf16
bnb_4bit_use_double_quant=True # Nested quantization
)
# Load quantized model
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-3.1-70B",
quantization_config=bnb_config,
device_map="auto"
)
# Prepare for training (enables gradient checkpointing)
model = prepare_model_for_kbit_training(model)
# LoRA config for QLoRA
lora_config = LoraConfig(
r=64, # Higher rank for 70B
lora_alpha=128,
lora_dropout=0.1,
target_modules=["q_proj", "v_proj", "k_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
bias="none",
task_type="CAUSAL_LM"
)
model = get_peft_model(model, lora_config)
# 70B model now fits on single 24GB GPU!
```
## LoRA parameter selection
### Rank (r) - capacity vs efficiency
| Rank | Trainable Params | Memory | Quality | Use Case |
|------|-----------------|--------|---------|----------|
| 4 | ~3M | Minimal | Lower | Simple tasks, prototyping |
| **8** | ~7M | Low | Good | **Recommended starting point** |
| **16** | ~14M | Medium | Better | **General fine-tuning** |
| 32 | ~27M | Higher | High | Complex tasks |
| 64 | ~54M | High | Highest | Domain adaptation, 70B models |
### Alpha (lora_alpha) - scaling factor
```python
# Rule of thumb: alpha = 2 * rank
LoraConfig(r=16, lora_alpha=32) # Standard
LoraConfig(r=16, lora_alpha=16) # Conservative (lower learning rate effect)
LoraConfig(r=16, lora_alpha=64) # Aggressive (higher learning rate effect)
```
### Target modules by architecture
```python
# Llama / Mistral / Qwen
target_modules = ["q_proj", "v_proj", "k_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
# GPT-2 / GPT-Neo
target_modules = ["c_attn", "c_proj", "c_fc"]
# Falcon
target_modules = ["query_key_value", "dense", "dense_h_to_4h", "dense_4h_to_h"]
# BLOOM
target_modules = ["query_key_value", "dense", "dense_h_to_4h", "dense_4h_to_h"]
# Auto-detect all linear layers
target_modules = "all-linear" # PEFT 0.6.0+
```
## Loading and merging adapters
### Load trained adapter
```python
from peft import PeftModel, AutoPeftModelForCausalLM
from transformers import AutoModelForCausalLM
# Option 1: Load with PeftModel
base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B")
model = PeftModel.from_pretrained(base_model, "./lora-llama-adapter")
# Option 2: Load directly (recommended)
model = AutoPeftModelForCausalLM.from_pretrained(
"./lora-llama-adapter",
device_map="auto"
)
```
### Merge adapter into base model
```python
# Merge for deployment (no adapter overhead)
merged_model = model.merge_and_unload()
# Save merged model
merged_model.save_pretrained("./llama-merged")
tokenizer.save_pretrained("./llama-merged")
# Push to Hub
merged_model.push_to_hub("username/llama-finetuned")
```
### Multi-adapter serving
```python
from peft import PeftModel
# Load base with first adapter
model = AutoPeftModelForCausalLM.from_pretrained("./adapter-task1")
# Load additional adapters
model.load_adapter("./adapter-task2", adapter_name="task2")
model.load_adapter("./adapter-task3", adapter_name="task3")
# Switch between adapters at runtime
model.set_adapter("task1") # Use task1 adapter
output1 = model.generate(**inputs)
model.set_adapter("task2") # Switch to task2
output2 = model.generate(**inputs)
# Disable adapters (use base model)
with model.disable_adapter():
base_output = model.generate(**inputs)
```
## PEFT methods comparison
| Method | Trainable % | Memory | Speed | Best For |
|--------|------------|--------|-------|----------|
| **LoRA** | 0.1-1% | Low | Fast | General fine-tuning |
| **QLoRA** | 0.1-1% | Very Low | Medium | Memory-constrained |
| AdaLoRA | 0.1-1% | Low | Medium | Automatic rank selection |
| IA3 | 0.01% | Minimal | Fastest | Few-shot adaptation |
| Prefix Tuning | 0.1% | Low | Medium | Generation control |
| Prompt Tuning | 0.001% | Minimal | Fast | Simple task adaptation |
| P-Tuning v2 | 0.1% | Low | Medium | NLU tasks |
### IA3 (minimal parameters)
```python
from peft import IA3Config
ia3_config = IA3Config(
target_modules=["q_proj", "v_proj", "k_proj", "down_proj"],
feedforward_modules=["down_proj"]
)
model = get_peft_model(model, ia3_config)
# Trains only 0.01% of parameters!
```
### Prefix Tuning
```python
from peft import PrefixTuningConfig
prefix_config = PrefixTuningConfig(
task_type="CAUSAL_LM",
num_virtual_tokens=20, # Prepended tokens
prefix_projection=True # Use MLP projection
)
model = get_peft_model(model, prefix_config)
```
## Integration patterns
### With TRL (SFTTrainer)
```python
from trl import SFTTrainer, SFTConfig
from peft import LoraConfig
lora_config = LoraConfig(r=16, lora_alpha=32, target_modules="all-linear")
trainer = SFTTrainer(
model=model,
args=SFTConfig(output_dir="./output", max_seq_length=512),
train_dataset=dataset,
peft_config=lora_config, # Pass LoRA config directly
)
trainer.train()
```
### With Axolotl (YAML config)
```yaml
# axolotl config.yaml
adapter: lora
lora_r: 16
lora_alpha: 32
lora_dropout: 0.05
lora_target_modules:
- q_proj
- v_proj
- k_proj
- o_proj
lora_target_linear: true # Target all linear layers
```
### With vLLM (inference)
```python
from vllm import LLM
from vllm.lora.request import LoRARequest
# Load base model with LoRA support
llm = LLM(model="meta-llama/Llama-3.1-8B", enable_lora=True)
# Serve with adapter
outputs = llm.generate(
prompts,
lora_request=LoRARequest("adapter1", 1, "./lora-adapter")
)
```
## Performance benchmarks
### Memory usage (Llama 3.1 8B)
| Method | GPU Memory | Trainable Params |
|--------|-----------|------------------|
| Full fine-tuning | 60+ GB | 8B (100%) |
| LoRA r=16 | 18 GB | 14M (0.17%) |
| QLoRA r=16 | 6 GB | 14M (0.17%) |
| IA3 | 16 GB | 800K (0.01%) |
### Training speed (A100 80GB)
| Method | Tokens/sec | vs Full FT |
|--------|-----------|------------|
| Full FT | 2,500 | 1x |
| LoRA | 3,200 | 1.3x |
| QLoRA | 2,100 | 0.84x |
### Quality (MMLU benchmark)
| Model | Full FT | LoRA | QLoRA |
|-------|---------|------|-------|
| Llama 2-7B | 45.3 | 44.8 | 44.1 |
| Llama 2-13B | 54.8 | 54.2 | 53.5 |
## Common issues
### CUDA OOM during training
```python
# Solution 1: Enable gradient checkpointing
model.gradient_checkpointing_enable()
# Solution 2: Reduce batch size + increase accumulation
TrainingArguments(
per_device_train_batch_size=1,
gradient_accumulation_steps=16
)
# Solution 3: Use QLoRA
from transformers import BitsAndBytesConfig
bnb_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4")
```
### Adapter not applying
```python
# Verify adapter is active
print(model.active_adapters) # Should show adapter name
# Check trainable parameters
model.print_trainable_parameters()
# Ensure model in training mode
model.train()
```
### Quality degradation
```python
# Increase rank
LoraConfig(r=32, lora_alpha=64)
# Target more modules
target_modules = "all-linear"
# Use more training data and epochs
TrainingArguments(num_train_epochs=5)
# Lower learning rate
TrainingArguments(learning_rate=1e-4)
```
## Best practices
1. **Start with r=8-16**, increase if quality insufficient
2. **Use alpha = 2 * rank** as starting point
3. **Target attention + MLP layers** for best quality/efficiency
4. **Enable gradient checkpointing** for memory savings
5. **Save adapters frequently** (small files, easy rollback)
6. **Evaluate on held-out data** before merging
7. **Use QLoRA for 70B+ models** on consumer hardware
## References
- **[Advanced Usage](references/advanced-usage.md)** - DoRA, LoftQ, rank stabilization, custom modules
- **[Troubleshooting](references/troubleshooting.md)** - Common errors, debugging, optimization
## Resources
- **GitHub**: https://github.com/huggingface/peft
- **Docs**: https://huggingface.co/docs/peft
- **LoRA Paper**: arXiv:2106.09685
- **QLoRA Paper**: arXiv:2305.14314
- **Models**: https://huggingface.co/models?library=peft