---
base_model: facebook/opt-350m
inference: false
model_creator: facebook
model_name: opt-350m
model_type: opt
pipeline_tag: text-generation
quantized_by: iproskurina
tags:
- pretrained
license: other
language:
- en
datasets:
- c4
---


<img src="https://cdn-uploads.huggingface.co/production/uploads/629a3dbcd496c6dcdebf41cc/t-6kpqFpEYJPT6zmvnm49.png" width="200" />

# OPT-350M - GPTQ


- Model creator: [Meta AI](https://huggingface.co/facebook)
- Original model: [OPT-350M](https://huggingface.co/facebook/opt-350m)

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

```shell
pip install accelerate==0.26.1 datasets==2.16.1 dill==0.3.7 gekko==1.0.6 multiprocess==0.70.15 peft==0.7.1 rouge==1.0.1 sentencepiece==0.1.99
git clone https://github.com/upunaprosk/AutoGPTQ
cd AutoGPTQ
pip install -v .
```
Recommended transformers version: 4.35.2.

### 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-350m-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-350m/blob/main/LICENSE.md)