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---
base_model: bigscience/bloom-560m
language:
- en
license: bigscience-bloom-rail-1.0
model_name: bloom-560m
pipeline_tag: text-generation
quantized_by: iproskurina
tags:
- gptq
- 4-bit
base_model_relation: quantized
inference: false
model_creator: bigscience
model_type: bloom
---
# 🌸 BLOOM 560M - GPTQ
- Model creator: [BigScience](https://huggingface.co/bigscience)
- Original model: [BLOOM 560M](https://huggingface.co/bigscience/bloom-560m)
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/bloom-560m-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"])
```
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