[docs/gpt-j] addd instructions for how minimize CPU RAM usage (#13795)

* add a note about tokenizer

* add  tips to load model is less RAM

* fix link

* fix more links

docs/source/model_doc/gptj.rst

@@ -24,15 +24,34 @@ This model was contributed by `Stella Biderman <https://huggingface.co/stellaath
 
 Tips:
 
-- Running [GPT-J](https://huggingface.co/EleutherAI/gpt-j-6B) in float32 precision on GPU requires at least 24 GB of
-  RAM. On GPUs with less than 24 GB RAM, one should therefore load the model in half-precision:
+- To load `GPT-J <https://huggingface.co/EleutherAI/gpt-j-6B>`__ in float32 one would need at least 2x model size CPU
+  RAM: 1x for initial weights and another 1x to load the checkpoint. So for GPT-J it would take at least 48GB of CPU
+  RAM to just load the model. To reduce the CPU RAM usage there are a few options. The ``torch_dtype`` argument can be
+  used to initialize the model in half-precision. And the ``low_cpu_mem_usage`` argument can be used to keep the RAM
+  usage to 1x. There is also a `fp16 branch <https://huggingface.co/EleutherAI/gpt-j-6B/tree/float16>`__ which stores
+  the fp16 weights, which could be used to further minimize the RAM usage. Combining all this it should take roughly
+  12.1GB of CPU RAM to load the model.
 
 .. code-block::
 
     >>> from transformers import GPTJForCausalLM
     >>> import torch
 
-    >>> model =  GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", torch_dtype=torch.float16)
+    >>> model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", revision="float16", torch_dtype=torch.float16, low_cpu_mem_usage=True)
+
+
+- The model should fit on 16GB GPU for inference. For training/fine-tuning it would take much more GPU RAM. Adam
+  optimizer for example makes four copies of the model: model, gradients, average and squared average of the gradients.
+  So it would need at least 4x model size GPU memory, even with mixed precision as gradient updates are in fp32. This
+  is not including the activations and data batches, which would again require some more GPU RAM. So one should explore
+  solutions such as DeepSpeed, to train/fine-tune the model. Another option is to use the original codebase to
+  train/fine-tune the model on TPU and then convert the model to Transformers format for inference. Instructions for
+  that could be found `here <https://github.com/kingoflolz/mesh-transformer-jax/blob/master/howto_finetune.md>`__
+
+- Although the embedding matrix has a size of 50400, only 50257 entries are used by the GPT-2 tokenizer. These extra
+  tokens are added for the sake of efficiency on TPUs. To avoid the mis-match between embedding matrix size and vocab
+  size, the tokenizer for `GPT-J <https://huggingface.co/EleutherAI/gpt-j-6B>`__ contains 143 extra tokens
+  ``<|extratoken_1|>... <|extratoken_143|>``, so the ``vocab_size`` of tokenizer also becomes 50400.
 
 - Although the embedding matrix has a size of 50400, only 50257 entries are used by the GPT-2 tokenizer. These extra
   tokens are added for the sake of efficiency on TPUs. To avoid the mis-match between embedding matrix size and vocab