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RichardErkhov/antoinelouis_-_belgpt2-4bits
antoinelouis_-_belgpt2-4bits is a text generation model from RichardErkhov. Use it when you need the model to write or continue text. It is set up for transformers.
Downloads · 30 days
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From the Hugging Face model README
Quantization made by Richard Erkhov.
belgpt2 - bnb 4bits
language:
The 1st GPT-2 model pre-trained on a very large and heterogeneous French corpus (~60Gb).
You can use BelGPT-2 with 🤗 transformers:
import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel
# Load pretrained model and tokenizer
model = GPT2LMHeadModel.from_pretrained("antoiloui/belgpt2")
tokenizer = GPT2Tokenizer.from_pretrained("antoiloui/belgpt2")
# Generate a sample of text
model.eval()
output = model.generate(
bos_token_id=random.randint(1,50000),
do_sample=True,
top_k=50,
max_length=100,
top_p=0.95,
num_return_sequences=1
)
# Decode it
decoded_output = []
for sample in output:
decoded_output.append(tokenizer.decode(sample, skip_special_tokens=True))
print(decoded_output)
Below is the list of all French copora used to pre-trained the model:
| Dataset | $corpus_name | Raw size | Cleaned size |
|---|---|---|---|
| CommonCrawl | common_crawl | 200.2 GB | 40.4 GB |
| NewsCrawl | news_crawl | 10.4 GB | 9.8 GB |
| Wikipedia | wiki | 19.4 GB | 4.1 GB |
| Wikisource | wikisource | 4.6 GB | 2.3 GB |
| Project Gutenberg | gutenberg | 1.3 GB | 1.1 GB |
| EuroParl | europarl | 289.9 MB | 278.7 MB |
| NewsCommentary | news_commentary | 61.4 MB | 58.1 MB |
| Total | 236.3 GB | 57.9 GB |
Detailed documentation on the pre-trained model, its implementation, and the data can be found here.
For attribution in academic contexts, please cite this work as:
@misc{louis2020belgpt2,
author = {Louis, Antoine},
title = {{BelGPT-2: A GPT-2 Model Pre-trained on French Corpora}},
year = {2020},
howpublished = {\url{https://github.com/ant-louis/belgpt2}},
}