


# 🌸 BLOOM 7b1 - GPTQ
- Model creator: [BigScience](https://huggingface.co/bigscience)
- Original model: [BLOOM 7b1](https://huggingface.co/bigscience/bloom-7b1)

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/bloom-7b1-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"])
```

{"language": ["ak", "ar", "as", "bm", "bn", "ca", "code", "en", "es", "eu", "fon", "fr", "gu", "hi", "id", "ig", "ki", "kn", "lg", "ln", "ml", "mr", "ne", "nso", "ny", "or", "pa", "pt", "rn", "rw", "sn", "st", "sw", "ta", "te", "tn", "ts", "tum", "tw", "ur", "vi", "wo", "xh", "yo", "zh", "zhs", "zht", "zu"], "license": "bigscience-bloom-rail-1.0", "tags": ["pretrained"], "datasets": ["c4"], "model_name": "bloom-7b1", "base_model": "bigscience/bloom-7b1", "inference": false, "model_creator": "bigscience", "model_type": "bloom", "pipeline_tag": "text-generation", "quantized_by": "iproskurina"}
transformers safetensors bloom text-generation pretrained ak ar as bm bn ca code en es eu fon fr gu hi id ig ki kn lg ln ml mr ne nso ny or pa pt rn rw sn st sw ta te tn ts tum tw ur vi wo xh yo zh zhs zht zu dataset:c4 arxiv:2210.17323 base_model:bigscience/bloom-7b1 license:bigscience-bloom-rail-1.0 autotrain_compatible text-generation-inference region:us