license: apache-2.0
language:
- ar
- hi
- id
pipeline_tag: text-generation
tags:
- multilingual
widget:
- text: في مدرستي السابقة
example_title: Arabic prompt
- text: आप समुद्री लुटेरों के बारे में क्या जानते हैं?
example_title: Hindi prompt
- text: Kucing saya suka
example_title: Indonesian prompt
mGPT-quantized
The concept: 8-bit quantized version of mGPT, a 1.3B param model released by AI-Forever / Sberbank AI in April 2022.
On the GPT scale, it is a similar # of parameters to GPT2-XL, but on 60+ languages.
AI-Forever also released a 13B-parameter model. I made an 8-bit quantized version with weights available here: https://huggingface.co/monsoon-nlp/mGPT-13B-quantized
My goal is to evaluate this on Arabic, Hindi, and Indonesian tasks, where there are fewer autoregressive language models in this size range.
For English: use a GPT model or LLaMa2-7B
In August 2023 AI-Forever added 1.3B-param models for about 1/3 of the model's languages. If your language is Mongolian, for example, use mGPT-1.3B-mongol and not this one.
How was the model created?
Quantization of mGPT 1.3B was done using bitsandbytes library:
from transformers import BitsAndBytesConfig, GPT2LMHeadModel
quantization_config = BitsAndBytesConfig(
load_in_8bit=True,
bnb_8bit_compute_dtype=torch.bfloat16,
bnb_8bit_use_double_quant=True,
bnb_8bit_quant_type="nf4",
)
qmodel = GPT2LMHeadModel.from_pretrained(
"ai-forever/mGPT",
load_in_8bit=True,
torch_dtype=torch.bfloat16,
quantization_config=quantization_config,
device_map="auto"
)
qmodel.save_pretrained("model_name")
Future steps
- mGPT could be further quantized (4-bit), but
model.save_pretrained()currently throws aNotImplementedErrorerror.