|
--- |
|
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"]) |
|
``` |
|
|
