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<h1 align="center">GPTQModel</h1> <p align="center">Production ready LLM model compression/quantization toolkit with accelerated inference support for both cpu/gpu via HF, vLLM, and SGLang.</p> <p align="center"> <a href="https://github.com/ModelCloud/GPTQModel/releases" style="text-decoration:none;"><img alt="GitHub release" src="https://img.shields.io/github/release/ModelCloud/GPTQModel.svg"></a> <a href="https://pypi.org/project/gptqmodel/" style="text-decoration:none;"><img alt="PyPI - Version" src="https://img.shields.io/pypi/v/gptqmodel"></a> <a href="https://pypi.org/project/gptqmodel/" style="text-decoration:none;"><img alt="PyPI - Downloads" src="https://img.shields.io/pypi/dm/gptqmodel"></a> </p>

News

<details> <summary>Archived News:</summary> * 08/10/2024 🚀 [v0.9.11](https://github.com/ModelCloud/GPTQModel/releases/tag/v0.9.11) Added LG EXAONE 3.0 model support. New `dynamic` per layer/module flexible quantization where each layer/module may have different bits/params. Added proper sharding support to `backend.BITBLAS`. Auto-heal quantization errors due to small damp values. </details>

Why should you use GPTQModel?

GPTQModel started out as a major refractor (fork) of AutoGTQP but has now morphed into a full-stand-in replacement with cleaner api, up-to-date model support, faster inference, faster quantization, higher quality quants and a pledge that ModelCloud, together with the open-source ML community, will take every effort to bring the library up-to-date with latest advancements and model support.

Features

Model Support: 🚀 (Added by GPTQModel)

🤗 Pre-quantized models on HF

Model
Baichuan✅Falon✅Llama 3.2 Vision🚀Qwen✅
Bloom✅Gemma 2🚀LongLLaMA✅Qwen2MoE🚀
ChatGLM🚀GPTBigCod✅MiniCPM3🚀RefinedWeb✅
CodeGen✅GPTNeoX✅Mistral✅StableLM✅
Cohere✅GPT-2✅Mixtral✅StarCoder2✅
DBRX Converted🚀GPT-J✅MobileLLM🚀XVERSE✅
Deci✅Granite🚀MOSS✅Yi✅
DeepSeek-V2🚀GRIN-MoE🚀MPT✅
DeepSeek-V2-Lite🚀InternLM 1/2.5🚀OPT✅
EXAONE 3.0🚀Llama 1/2/3✅Phi/Phi-3🚀

Quality: Quantized Llama-3.2-Instruct models with 100% avg recovery:

image

Platform Requirements

GPTQModel is validated for Linux x86_64 with Nvidia GPUs. Windows WSL2 may work but un-tested.

Install

PIP

# You can install optional modules like autoround, ipex, vllm, sglang, or bitblas. Example: pip install -v --no-build-isolation gptqmodel[auto_round,vllm,sglang,bitblas,ipex]
pip install -v gptqmodel --no-build-isolation

Install from source

# clone repo
git clone https://github.com/ModelCloud/GPTQModel.git && cd GPTQModel

# pip: compile and install
# You can install optional modules like autoround, ipex, vllm, sglang, or bitblas. Example: pip install -v --no-build-isolation .[auto_round,vllm,sglang,bitblas,ipex]
pip install -v . --no-build-isolation

# uv: compile and install 
uv pip install -v . --no-build-isolation

Script installation

# You can pass modules as arguments, e.g., --vllm --sglang --bitblas. Example: bash install.sh --vllm --sglang --bitblas
bash install.sh

Quantization and Inference

warning: this is just a showcase of the usage of basic apis in GPTQModel, which uses only one sample to quantize a much small model, quality of quantized model using such little samples may not good.

Below is an example for the simplest use of gptqmodel to quantize a model and inference after quantization:

from transformers import AutoTokenizer
from gptqmodel import GPTQModel, QuantizeConfig

model_id = "facebook/opt-125m"
quant_path = "opt-125m-gptqmodel-4bit"

tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
calibration_dataset = [
    tokenizer(
        "The world is a wonderful place full of beauty and love."
    )
]

quant_config = QuantizeConfig(
    bits=4,  # 4-bit
    group_size=128,  # 128 is good balance between quality and performance
)

# load un-quantized model into into cpu memory
model = GPTQModel.load(model_id, quant_config)

# quantize model
model.quantize(calibration_dataset)

# save quantized model
model.save(quant_path)

# load quantized model to the first GPU
model = GPTQModel.load(quant_path)

# inference with model.generate
print(tokenizer.decode(model.generate(**tokenizer("gptqmodel is", return_tensors="pt").to(model.device))[0]))

For more advanced features of model quantization, please reference to this script

How to Add Support for a New Model

Read the gptqmodel/models/llama.py code which explains in detail via comments how the model support is defined. Use it as guide to PR for to new models. Most models follow the same pattern.

Evaluation on Downstream Tasks

You can use tasks defined in gptqmodel.eval_tasks to evaluate model's performance on specific down-stream task before and after quantization.

The predefined tasks support all causal-language-models implemented in 🤗 transformers and in this project.

<details> <summary>Below is an example to evaluate `EleutherAI/gpt-j-6b` on sequence-classification task using `cardiffnlp/tweet_sentiment_multilingual` dataset:</summary> 
from functools import partial

import datasets
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
from gptqmodel import GPTQModel, QuantizeConfig
from gptqmodel.eval_tasks import SequenceClassificationTask

MODEL = "EleutherAI/gpt-j-6b"
DATASET = "cardiffnlp/tweet_sentiment_multilingual"
TEMPLATE = "Question:What's the sentiment of the given text? Choices are {labels}.\nText: {text}\nAnswer:"
ID2LABEL = {
    0: "negative",
    1: "neutral",
    2: "positive"
}
LABELS = list(ID2LABEL.values())


def ds_refactor_fn(samples):
    text_data = samples["text"]
    label_data = samples["label"]

    new_samples = {"prompt": [], "label": []}
    for text, label in zip(text_data, label_data):
        prompt = TEMPLATE.format(labels=LABELS, text=text)
        new_samples["prompt"].append(prompt)
        new_samples["label"].append(ID2LABEL[label])

    return new_samples


#  model = AutoModelForCausalLM.from_pretrained(MODEL).eval().half().to("cuda:0")
model = GPTQModel.load(MODEL, QuantizeConfig())
tokenizer = AutoTokenizer.from_pretrained(MODEL)

task = SequenceClassificationTask(
    model=model,
    tokenizer=tokenizer,
    classes=LABELS,
    data_name_or_path=DATASET,
    prompt_col_name="prompt",
    label_col_name="label",
    **{
        "num_samples": 1000,  # how many samples will be sampled to evaluation
        "sample_max_len": 1024,  # max tokens for each sample
        "block_max_len": 2048,  # max tokens for each data block
        # function to load dataset, one must only accept data_name_or_path as input
        # and return datasets.Dataset
        "load_fn": partial(datasets.load_dataset, name="english"),
        # function to preprocess dataset, which is used for datasets.Dataset.map,
        # must return Dict[str, list] with only two keys: [prompt_col_name, label_col_name]
        "preprocess_fn": ds_refactor_fn,
        # truncate label when sample's length exceed sample_max_len
        "truncate_prompt": False
    }
)

# note that max_new_tokens will be automatically specified internally based on given classes
print(task.run())

# self-consistency
print(
    task.run(
        generation_config=GenerationConfig(
            num_beams=3,
            num_return_sequences=3,
            do_sample=True
        )
    )
)
</details>

Learn More

tutorials provide step-by-step guidance to integrate gptqmodel with your own project and some best practice principles.

examples provide plenty of example scripts to use gptqmodel in different ways.

Supported Evaluation Tasks

Currently, gptqmodel supports: LanguageModelingTask, SequenceClassificationTask and TextSummarizationTask; more Tasks will come soon!

Which kernel is used by default?

GPTQModel will use Marlin, Exllama v2, Triton kernels in that order for maximum inference performance.

Cite

@article{frantar2024marlin,
  title={MARLIN: Mixed-Precision Auto-Regressive Parallel Inference on Large Language Models},
  author={Frantar, Elias and Castro, Roberto L and Chen, Jiale and Hoefler, Torsten and Alistarh, Dan},
  journal={arXiv preprint arXiv:2408.11743},
  year={2024}
}

@article{frantar-gptq,
  title={{GPTQ}: Accurate Post-training Compression for Generative Pretrained Transformers}, 
  author={Elias Frantar and Saleh Ashkboos and Torsten Hoefler and Dan Alistarh},
  year={2022},
  journal={arXiv preprint arXiv:2210.17323}
}