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license: bigscience-bloom-rail-1.0
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
pipeline_tag: text-generation
---
<h1 style='text-align: center '>BLOOM LM - 8bit</h1>
<h2 style='text-align: center '><em>BigScience Large Open-science Open-access Multilingual Language Model - 8bit</em> </h2>
<h3 style='text-align: center '>Model Card</h3>
<img src="https://s3.amazonaws.com/moonup/production/uploads/1657124309515-5f17f0a0925b9863e28ad517.png" alt="BigScience Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
Version 1.0 / 26.May.2022
Related paper: https://arxiv.org/abs/2208.07339
## TL;DR
This repository contains 8bit weights of `bloom-1b7` model. You can load this model using `transformers==4.28.0` and `bitsandbytes>0.37.2` out of the box !
```python
# pip install accelerate bitsandbytes
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("ybelkada/bloom-1b7-8bit")
```
## How to push 8bit weights?
First, make sure you are using `transformers` & `bitsandbytes` versions stated above. Then load your 8bit model as usual using `load_in_8bit=True`!
```python
# pip install accelerate bitsandbytes
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("bigscience/bloom-1b7", device_map="auto", load_in_8bit=True)
```
Then just call `push_to_hub` method or `save_pretrained` method if you want to save your 8bit model locally
```python
model.push_to_hub("{your_username}/bloom-1b7-8bit")
```
That's it!
## What is inside the model's `state_dict`?
Inside the state dict of the model (`pytorch_model.bin` file) you have
- the quantized `int8` weights
- the quantization statistics in `float16`
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