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base_model: facebook/opt-2.7b
language:
- en
license: other
model_name: opt-2.7b
pipeline_tag: text-generation
inference: false
model_creator: facebook
model_type: opt
quantized_by: iproskurina
tags:
- gptq
- 4-bit
base_model_relation: quantized
---
<img src="https://cdn-uploads.huggingface.co/production/uploads/629a3dbcd496c6dcdebf41cc/t-6kpqFpEYJPT6zmvnm49.png" width="200" />
# OPT-2.7B - GPTQ
- Model creator: [Meta AI](https://huggingface.co/facebook)
- Original model: [OPT-2.7B](https://huggingface.co/facebook/opt-2.7b)
The model published in this repo was quantized to 4bit using [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ).
**Quantization details**
**All quantization parameters were taken from [GPTQ paper](https://arxiv.org/abs/2210.17323).**
GPTQ calibration data consisted of 128 random 2048 token segments from the [C4 dataset](https://huggingface.co/datasets/c4).
The grouping size used for quantization is equal to 128.
## How to use this GPTQ model from Python code
### Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install --upgrade transformers optimum
# If using PyTorch 2.1 + CUDA 12.x:
pip3 install --upgrade auto-gptq
# or, if using PyTorch 2.1 + CUDA 11.x:
pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
```
If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.5.1
pip3 install .
```
### You can then use the following code
```python
from transformers import AutoTokenizer, TextGenerationPipeline,AutoModelForCausalLM
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
pretrained_model_dir = "iproskurina/opt-2.7b-gptq-4bit"
tokenizer = AutoTokenizer.from_pretrained(pretrained_model_dir, use_fast=True)
model = AutoGPTQForCausalLM.from_quantized(pretrained_model_dir, device="cuda:0", model_basename="model")
pipeline = TextGenerationPipeline(model=model, tokenizer=tokenizer)
print(pipeline("auto-gptq is")[0]["generated_text"])
```
[**LICENSE**](https://huggingface.co/facebook/opt-2.7b/blob/main/LICENSE.md)
### Run the model with GPTQModel
GPTQModel package: https://github.com/ModelCloud/GPTQModel
```
pip install -v gptqmodel=="1.8.0" --no-build-isolation
from gptqmodel import GPTQModel
model_id = 'iproskurina/opt-2.7b-GPTQ-4bit-g128'
model = GPTQModel.load(model_id)
result = model.generate("Uncovering deep insights")[0] # tokens
print(model.tokenizer.decode(result)) # string output
```
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