peft-fine-tuning

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.

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Repositório: davila7/claude-code-templates
Última atividade na origem: 8 de janeiro de 2026 às 14:24
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# Basic installation
pip install peft

# With quantization support (recommended)
pip install peft bitsandbytes

# Full stack
pip install peft transformers accelerate bitsandbytes datasets

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")

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!

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

# 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)

# 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+

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 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")

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)

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

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!

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)

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()

# 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

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")
)

# 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")

# 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()

# 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)

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"]

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"]

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%)

Method	Tokens/sec	vs Full FT
Full FT	2,500	1x
LoRA	3,200	1.3x
QLoRA	2,100	0.84x

Model	Full FT	LoRA	QLoRA
Llama 2-7B	45.3	44.8	44.1
Llama 2-13B	54.8	54.2	53.5

peft-fine-tuning

Opções de instalação

Revise os arquivos de origem

PEFT (Parameter-Efficient Fine-Tuning)

When to use PEFT

PEFT (Parameter-Efficient Fine-Tuning)

When to use PEFT

Quick start

Installation

LoRA fine-tuning (standard)

QLoRA fine-tuning (memory-efficient)

LoRA parameter selection

Rank (r) - capacity vs efficiency

Alpha (lora_alpha) - scaling factor

Target modules by architecture

Loading and merging adapters

Load trained adapter

Merge adapter into base model

Multi-adapter serving

PEFT methods comparison

IA3 (minimal parameters)

Prefix Tuning

Integration patterns

With TRL (SFTTrainer)

With Axolotl (YAML config)

With vLLM (inference)

Performance benchmarks

Memory usage (Llama 3.1 8B)

Training speed (A100 80GB)

Quality (MMLU benchmark)

Common issues

CUDA OOM during training

Adapter not applying

Quality degradation

Best practices

References

Resources

peft-fine-tuning

Informações da origem

Opções de instalação

Revise os arquivos de origem

Mais deste repositório

PEFT (Parameter-Efficient Fine-Tuning)

When to use PEFT

Mais deste repositório

PEFT (Parameter-Efficient Fine-Tuning)

When to use PEFT

Quick start

Installation

LoRA fine-tuning (standard)

QLoRA fine-tuning (memory-efficient)

LoRA parameter selection

Rank (r) - capacity vs efficiency

Alpha (lora_alpha) - scaling factor

Target modules by architecture

Loading and merging adapters

Load trained adapter

Merge adapter into base model

Multi-adapter serving

PEFT methods comparison

IA3 (minimal parameters)

Prefix Tuning

Integration patterns

With TRL (SFTTrainer)

With Axolotl (YAML config)

With vLLM (inference)

Performance benchmarks

Memory usage (Llama 3.1 8B)

Training speed (A100 80GB)

Quality (MMLU benchmark)

Common issues

CUDA OOM during training

Adapter not applying

Quality degradation

Best practices

References

Resources