Create README.md

README.md

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+# This repo shows how to convert a fairseq NLLB-MoE model to transformers and run a forward pass 
+
+As the `fairseq` repository is not really optimised to run inference out-of-the-box, make sure you have a very very big CPU/GPU RAM. 
+Around 600 GB are required to run an inference with the `fairseq` model, as you need to load the checkpoints (~300GB) then build the model (~300GB again), then finally you can load the checkpoints in the model.
+
+## 0. Download the original checkpoints:
+The checkpoints in this repository were obtained using the following command (ased on the instructions given on the fairseq repository): 
+```bash
+wget --trust-remote-name path_to_nllb
+tar -cf model.tar.zf
+```
+
+The NLLB checkpoints should noz 
+
+## 1. Install PyTorch
+Use the following command:
+```bash
+pip3 install torch==1.10.1+cu113 torchvision==0.11.2+cu113 torchaudio==0.10.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
+```
+
+## 2. Install fairseq
+```bash
+git clone https://github.com/facebookresearch/fairseq.git
+cd fairscale
+git checkout prefetch_fsdp_params_simple
+pip3 install -e .
+```
+
+## 3. Clone this repo (click top right on "How to clone")
+
+## 4. Run the inference script:
+Convert the checkpoints on the fly using the conversion script. `transformers` is required to do this: 
+```bash
+cd <path/to/cloned/repo>
+python3 /home/arthur_huggingface_co/fairseq/weights/checkpoints/convert_nllb_moe_sharded_original_checkpoint_to_pytorch.py --pytorch_dump_folder_path <dump_folder> --nllb_moe_checkpoint_path <nllb_checkpoint_path>
+```
+## 4. Run the inference script:
+```bash
+cd <path/to/cloned/repo>
+bash run.sh
+```