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few more nits

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Awni Hannun 2023-12-09 14:20:19 -08:00
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8
lora/README.md
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@ -27,10 +27,10 @@ If you do not have access to the Llama weights you will need to [request
access](https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform)
from Meta.
Convert the weights with:
Convert the model with:
```
python convert.py <path_to_torch_weights> <path_to_mlx_weights.npz>
python convert.py <path_to_torch_model> <path_to_mlx_model>
```
## Run
@ -52,7 +52,7 @@ python lora.py --model <path_to_model> \
```
Note, the model path should have the MLX weights, the tokenizer, and the
`params.json` configuration which will all be output by the `conver.py` script.
`params.json` configuration which will all be output by the `convert.py` script.
By default, the adapter weights are saved in `adapters.npz`. You can specify
the output location with `--adapter_file`.
@ -96,8 +96,6 @@ training and validation loss at a few points over the course of training.
| 800 | 1.017 | 1.255 |
| 1000 | 1.070 | 1.230 |
After training for 1000 iterations, the validation perplexity reduces to XX.
The model trains at around 475 tokens per second on an M2 Ultra.
[^lora]: Refer to the [arXiv paper](https://arxiv.org/abs/2106.09685) for more details on LoRA.

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lora/requirements.txt
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@ -1,2 +1,3 @@
mlx
sentencepiece
torch