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---
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-13B](https://huggingface.co/ai-forever/mGPT-13B), an LLM released by AI-Forever / Sberbank AI in 2022-2023.
On the GPT scale, it is between the # of parameters for GPT-2 and GPT-3, but comparison is tricky after training on 60+ languages.
My goal is to evaluate this on Hindi and Indonesian tasks, where there are fewer autoregressive language models in this size range.
For English: use a GPT model or LLaMa2-7B
For Arabic: in August 2023 I would recommend the bilingual [JAIS model](https://huggingface.co/inception-mbzuai/jais-13b), which is also 13B parameters can be quantized.
In August 2023 AI-Forever added 1.3B-param models for 20+ languages. If your language is Mongolian, for example, it might be better to use mGPT-1.3B-mongol and not this one.
They also have a 1.3B param model for all languages, which I further quantized here: https://huggingface.co/monsoon-nlp/mGPT-quantized
## How was the model created?
Quantization of mGPT-13B was done using `bitsandbytes` library, CoLab Pro with an A100 GPU, and a lot of space on Google Drive.
```python
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-13B",
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 a `NotImplementedError` error. |