peftee

KO | EN

gitlite — search

#python #typescript #golang #android #linux #nodejs #windows #awesome #vue #pytorch #security #mcp #openai #open-source

(★ 11)

LLM fine-tuning library

peftee 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

<p align="center">
	<picture>
		<source media="(prefers-color-scheme: dark)" srcset="https://ollm.s3.us-east-1.amazonaws.com/peftee/logo_wide.png">
		<img alt="peftee" src="https://ollm.s3.us-east-1.amazonaws.com/peftee/logo_wide.png" width=60%>
	</picture>
</p>
<h3 align="center">
Efficient LLM fine-tuning with <i>much less </i> VRAM
</h3>

**peftee** (PEFT-ee) is a lightweight Python library for efficient LLM fine-tuning, built on top of Hugging Face Transformers and PyTorch. It enables fine-tuning models like **Llama3-8B** on **8 GB GPUs** with minimal speed loss ⚡ (**~9s** per 200 samples at 2k context length) while saving **~14 GB** (7.6 vs 21.8) of VRAM [▶️ Colab Notebook](https://colab.research.google.com/drive/1c1LlTm5md0vTXgqv0w6H1j7GO9prmps0?usp=sharing). No quantization is used — only **fp16/bf16** precision.

> 💡 **Intuition**  
> Today, LLM fine-tuning is mostly about adapting **style**, **structure**, and **behavior**, rather than inserting new knowledge — for that, **RAG** is a better approach. Moreover, in most cases, there’s no need to fine-tune all transformer layers; updating only the last few (typically 4–8) with an adapter such as **LoRA** is sufficient. **peftee** is built precisely for this scenario.

---
⭐ *How do we achieve this:*

- Intelligently using Disk (SSD preferable) and CPU offloading with minimal overhead
<p align="left">
	<img src="https://ollm.s3.us-east-1.amazonaws.com/peftee/scheme.jpg">
</p>

- Parameter efficient fine-tuning techniques like LoRA
- Gradient checkpointing
- Optimizer states offloading (experimental)
- FlashAttention-2 with online softmax. Full attention matrix is never materialized.
---

**Supported model families:** 
- ✅ Llama3 (ex, meta-llama/Meta-Llama-3-8B) 
- ✅ Gemma3 (ex, google/gemma-3-12b-it)

**Supported GPUs**: NVIDIA, AMD, and Apple Silicon (MacBook).

## Getting Started

It is recommended to create venv or conda environment first
```bash
python3 -m venv peftee_env
source peftee_env/bin/activate
```

Install peftee with `pip install peftee` or [from source](https://github.com/Mega4alik/peftee):

```bash
git clone https://github.com/Mega4alik/peftee.git
cd peftee
pip install --no-build-isolation -e .
```

## Usage
```bash
# download the model first
huggingface-cli download "meta-llama/Llama-3.2-1B" --local-dir "./models/Llama-3.2-1B/" --local-dir-use-symlinks False
```
Training sample
```python
import torch
from torch.utils.data import DataLoader
from datasets import load_dataset
from transformers import AutoTokenizer, TextStreamer
from peft import LoraConfig
from peftee import SFTTrainer, DefaultDataCollator

model_dir = "./models/Llama-3.2-1B/"
# initialize tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_dir)
tokenizer.pad_token = tokenizer.eos_token

# load dataset (sample)
def preprocess(ex):
		return {
			"prompt": f"Given schema {ex['schema']}, extract the fields from: {ex['text']}",
			"completion": ex["item"]
		}
dataset = load_dataset("paraloq/json_data_extraction")
dataset = dataset.map(preprocess, batched=False)
dataset = dataset.filter(lambda x: len(x["prompt"]) + len(x["completion"]) < 1500*5) #filter
dataset = dataset["train"].train_test_split(test_size=0.06, seed=42)
train_dataset, test_dataset = dataset["train"], dataset["test"]
print("Dataset train, test sizes:", len(train_dataset), len(test_dataset))

# Training
data_collator = DefaultDataCollator(tokenizer, is_eval=False, logging=True) #input: {prompt, completion}. output: {input_ids, attention_mask, labels}
peft_config = LoraConfig(
	target_modules=["self_attn.q_proj", "self_attn.v_proj"], # it will automatically apply to last trainable layers
	r=8, #8-32
	lora_alpha=16, #r*2 normally
	task_type="CAUSAL_LM"
)
trainer = SFTTrainer(
	model_dir,
	output_dir="./mymodel/",    
	device="cuda:0",
	trainable_layers_num=4, #4-8, last layers
	offload_cpu_layers_num=0, #99 for maximum offload to CPU
	peft_config=peft_config,
	epochs=3,
	samples_per_step=100, #100-500, depending on available RAM
	batch_size=2,
	gradient_accumulation_batch_steps=2,
	gradient_checkpointing=True,
	learning_rate=2e-4,
	eval_steps=4,
	save_steps=4,
	data_collator=data_collator,
	train_dataset=train_dataset,
	eval_dataset=test_dataset
)
trainer.train(resume_from_checkpoint=None) #checkpoint dir
```

For **Evaluation/Inference**, we will be using [oLLM](https://github.com/Mega4alik/ollm), LLM inference library

```bash
# Install ollm
pip install --no-build-isolation ollm
```

```python
from ollm import AutoInference
data_collator = DefaultDataCollator(tokenizer, is_eval=True, logging=False)
o = AutoInference(model_dir, adapter_dir="./mymodel/checkpoint-20/", device="cuda:0", logging=False)
text_streamer = TextStreamer(o.tokenizer, skip_prompt=True, skip_special_tokens=False)
test_ds = DataLoader(test_dataset, batch_size=1, shuffle=True)
for sample in test_ds:
	x = data_collator(sample)
	outputs = o.model.generate(input_ids=x["input_ids"].to(o.device), max_new_tokens=500, streamer=text_streamer).cpu()
	answer = o.tokenizer.decode(outputs[0][x["input_ids"].shape[-1]:], skip_special_tokens=False)
	print(answer)
```
## Contact us
If there’s a model you’d like to see supported, feel free to suggest it in the [discussion](https://github.com/Mega4alik/peftee/discussions/1) — I’ll do my best to make it happen.

peftee

peftee 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

peftee

peftee 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README