Create README.md

README.md

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+---
+language: fr
+license: mit
+tags:
+- causal-lm
+- fr 
+datasets:
+- fr_covid_news
+---
+
+### GPT-J COVID-19 French News with 8-bit weights
+
+
+This is a version of Cedille's GPT-J (fr-boris) with 6 billion parameters fine-tuned on [COVID-19 French News dataset](https://huggingface.co/datasets/gustavecortal/fr_covid_news) to generate headlines related to COVID-19. 
+
+You can generate the model in colab or equivalent desktop gpu (e.g. single 1080Ti) because the model has 8-bit weights. Inspired by [GPT-J 8bit](https://huggingface.co/hivemind/gpt-j-6B-8bit). 
+
+Here's how to run it: [![colab](https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667)](https://colab.research.google.com/drive/1lMja-CPc0vm5_-gXNXAWU-9c0nom7vZ9)
+
+This model can be easily loaded using the `GPTJForCausalLM` functionality:
+```python
+from transformers import GPTJForCausalLM
+model = GPTJForCausalLM.from_pretrained("gustavecortal/gpt-j-fr-covid-news")
+```
+
+Remember, you have to Monkey-Patch the model before loading it (see Colab above).
+
+## fr-boris
+
+Boris is a 6B parameter autoregressive language model based on the GPT-J architecture and trained using the [mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax) codebase.
+
+Boris was trained on around 78B tokens of French text from the [C4](https://huggingface.co/datasets/c4) dataset.
+
+## Links
+
+* [Gustave Cortal](https://twitter.com/gustavecortal)