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32 GB example

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@ -150,6 +150,19 @@ of memory. Here are some tips to reduce memory use should you need to do so:
you can do is break your examples into smaller you can do is break your examples into smaller
sequences when making the `{train, valid, test}.jsonl` files. sequences when making the `{train, valid, test}.jsonl` files.
For example, for a machine with 32 GB the following should run reasonably fast:
```
python lora.py \
--model <path_to_model> \
--train \
--batch-size 1 \
--lora-layers 4
```
On an M1 Max with 32 GB we process about 250 tokens-per-second.
[^lora]: Refer to the [arXiv paper](https://arxiv.org/abs/2106.09685) for more details on LoRA. [^lora]: Refer to the [arXiv paper](https://arxiv.org/abs/2106.09685) for more details on LoRA.
[^llama]: Refer to the [arXiv paper](https://arxiv.org/abs/2302.13971) and [blog post](https://ai.meta.com/blog/large-language-model-llama-meta-ai/) for more details. [^llama]: Refer to the [arXiv paper](https://arxiv.org/abs/2302.13971) and [blog post](https://ai.meta.com/blog/large-language-model-llama-meta-ai/) for more details.
[^mistral]: Refer to the [blog post](https://mistral.ai/news/announcing-mistral-7b/) and [github repository](https://github.com/mistralai/mistral-src) for more details. [^mistral]: Refer to the [blog post](https://mistral.ai/news/announcing-mistral-7b/) and [github repository](https://github.com/mistralai/mistral-src) for more details.