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Adding full finetuning (#903)

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* Adding full model weights finetuning

* Updating the LORA.md and ACKNOWLEDGMENTS.md files.

* removing --use-dora and --fulll-training and adding --fine-tune-type

* some clean up

* reformating and fixing dora training

* updated CONFIG_DEFAULTS

* update config example

* update in the config example fie

* Update LORA.md

* merge and commit

* adding argument for dora linear layer

* clean up

* clean up in the example yaml file

* fix

* final fix before sending

* small addition to re md file

* fix for loading the fully trained model by saving all the files and configs correctly

* clean up

* removing the unnesesairy files

* changing lora layers back to 16

* removed max file size

* nits

* resolve merge

* some consistency changes

---------

Co-authored-by: Awni Hannun <awni@apple.com>
This commit is contained in:
Gökdeniz Gülmez
2024-09-30 02:12:47 +02:00
committed by GitHub
parent 7ec2021bb9
commit 50e5ca81a8
9 changed files with 79 additions and 70 deletions
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16
llms/mlx_lm/LORA.md
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@@ -57,6 +57,9 @@ mlx_lm.lora \
--iters 600
```
To fine-tune the full model weights, add the `--fine-tune-type full` flag.
Currently supported fine-tuning types are `lora` (default), `dora`, and `full`.
The `--data` argument must specify a path to a `train.jsonl`, `valid.jsonl`
when using `--train` and a path to a `test.jsonl` when using `--test`. For more
details on the data format see the section on [Data](#Data).
@@ -67,8 +70,8 @@ mistralai/Mistral-7B-v0.1`.
If `--model` points to a quantized model, then the training will use QLoRA,
otherwise it will use regular LoRA.
By default, the adapter config and weights are saved in `adapters/`. You can
specify the output location with `--adapter-path`.
By default, the adapter config and learned weights are saved in `adapters/`.
You can specify the output location with `--adapter-path`.
You can resume fine-tuning with an existing adapter with
`--resume-adapter-file <path_to_adapters.safetensors>`.
@@ -118,7 +121,7 @@ mlx_lm.fuse --model <path_to_model>
```
This will by default load the adapters from `adapters/`, and save the fused
model in the path `lora_fused_model/`. All of these are configurable.
model in the path `fused_model/`. All of these are configurable.
To upload a fused model, supply the `--upload-repo` and `--hf-path` arguments
to `mlx_lm.fuse`. The latter is the repo name of the original model, which is
@@ -141,7 +144,7 @@ mlx_lm.fuse \
--export-gguf
```
This will save the GGUF model in `lora_fused_model/ggml-model-f16.gguf`. You
This will save the GGUF model in `fused_model/ggml-model-f16.gguf`. You
can specify the file name with `--gguf-path`.
## Data
@@ -301,7 +304,7 @@ of memory. Here are some tips to reduce memory use should you need to do so:
setting this to `2` or `1` will reduce memory consumption. This may slow
things down a little, but will also reduce the memory use.
3. Reduce the number of layers to fine-tune with `--lora-layers`. The default
3. Reduce the number of layers to fine-tune with `--num-layers`. The default
is `16`, so you can try `8` or `4`. This reduces the amount of memory
needed for back propagation. It may also reduce the quality of the
fine-tuned model if you are fine-tuning with a lot of data.
@@ -323,7 +326,7 @@ mlx_lm.lora \
--model mistralai/Mistral-7B-v0.1 \
--train \
--batch-size 1 \
--lora-layers 4 \
--num-layers 4 \
--data wikisql
```
@@ -333,4 +336,5 @@ tokens-per-second, using the MLX Example
data set.
[^lora]: Refer to the [arXiv paper](https://arxiv.org/abs/2106.09685) for more details on LoRA.
[^qlora]: Refer to the paper [QLoRA: Efficient Finetuning of Quantized LLMs](https://arxiv.org/abs/2305.14314)