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llm-tool

See various READMEs:

Building

Build the llm-tool scheme in Xcode.

Running: Xcode

To run this in Xcode simply press cmd-opt-r to set the scheme arguments. For example:

--model mlx-community/Mistral-7B-v0.1-hf-4bit-mlx
--prompt "swift programming language"
--max-tokens 50

Then cmd-r to run.

Note: you may be prompted for access to your Documents directory -- this is where the Hugging Face HubApi stores the downloaded files.

The model should be a path in the Hugging Face repository, e.g.:

  • mlx-community/Mistral-7B-v0.1-hf-4bit-mlx
  • mlx-community/phi-2-hf-4bit-mlx

See LLM for more info.

Running: Command Line

Use the mlx-run script to run the command line tools:

./mlx-run llm-tool --prompt "swift programming language"

By default this will find and run the tools built in Release configuration. Specify --debug to find and run the tool built in Debug configuration.

See also:

Troubleshooting

If the program crashes with a very deep stack trace you may need to build in Release configuration. This seems to depend on the size of the model.

There are a couple options:

  • build Release
  • force the model evaluation to run on the main thread, e.g. using @MainActor
  • build Cmlx with optimizations by modifying mlx/Package.swift and adding .unsafeFlags(["-O"]), around line 87

Building in Release / optimizations will remove a lot of tail calls in the C++ layer. These lead to the stack overflows.

See discussion here: #3

LoRA

llm-tool provides an example LoRA driver based on:

This is an example of using MLX to fine-tune an LLM with low rank adaptation (LoRA) for a target task.1 The example also supports quantized LoRA (QLoRA).2 The example works with Llama and Mistral style models available on Hugging Face.

In this example we'll use the WikiSQL3 dataset to train the LLM to generate SQL queries from natural language. However, the example is intended to be general should you wish to use a custom dataset.

Note: Some of the prompts have newlines in them which is difficult to achieve via running in Xcode.

Running llm-tool lora will produce help:

SUBCOMMANDS:
  train                   LoRA training
  fuse                    Fuse lora adapter weights back in to original model
  test                    LoRA testing
  eval                    LoRA evaluation

Training

The first step will be training the LoRA adapter. Example training data is available in $SRCROOT/Data/lora. You can use your own data in either jsonl or txt format with one entry per line.

We need to specify a number of parameters:

  • --model -- which model to use. This can be quantized 2 or not 1
  • --data -- directory with the test, train and valid files. These can be either jsonl or txt files
  • --adapter -- path to a safetensors file to write the fine tuned parameters into

Additionally the performance of the fine tuning can be controlled with:

  • --batch-size -- size of the minibatches to run in the training loop, e.g. how many prompts to process per iteration
  • --lora-layers -- the number of layers in the Attention section of the model to adapt and train
  • --iterations -- the number of iterations to train for

If desired, the amount of memory used can be adjusted with:

  • --cache-size -- the number shown below limits the cache size to 1024M

Here is an example run using adapters on the last 4 layers of the model:

./mlx-run llm-tool lora train \
    --model mlx-community/Mistral-7B-v0.1-hf-4bit-mlx \
    --data Data/lora \
    --adapter /tmp/lora-layers-4.safetensors \
    --batch-size 1 --lora-layers 4 \
    --cache-size 1024

giving output like this:

Model: mlx-community/Mistral-7B-v0.1-hf-4bit-mlx
Total parameters: 1,242M
Trainable parameters: 0.426M
Iteration 1: validation loss 2.443872, validation time 3.330629s
Iteration 10: training loss 2.356848, iterations/sec 2.640604, Tokens/sec 260.363581
Iteration 20: training loss 2.063395, iterations/sec 2.294999, Tokens/sec 232.483365
Iteration 30: training loss 1.63846, iterations/sec 2.279401, Tokens/sec 225.204788
Iteration 40: training loss 1.66366, iterations/sec 2.493669, Tokens/sec 218.196057
Iteration 50: training loss 1.470927, iterations/sec 2.301153, Tokens/sec 231.72614
Iteration 60: training loss 1.396581, iterations/sec 2.400012, Tokens/sec 230.401195
Iteration 70: training loss 1.587023, iterations/sec 2.422193, Tokens/sec 218.966258
Iteration 80: training loss 1.376895, iterations/sec 2.111973, Tokens/sec 216.477187
Iteration 90: training loss 1.245127, iterations/sec 2.383802, Tokens/sec 214.065436
Iteration 100: training loss 1.344523, iterations/sec 2.424746, Tokens/sec 223.076649
Iteration 100: validation loss 1.400582, validation time 3.489797s
Iteration 100: saved weights to /tmp/lora.safetensors
...
Iteration 910: training loss 1.181306, iterations/sec 2.355085, Tokens/sec 212.428628
Iteration 920: training loss 1.042286, iterations/sec 2.374377, Tokens/sec 222.479127
Iteration 930: training loss 0.920768, iterations/sec 2.475088, Tokens/sec 220.035347
Iteration 940: training loss 1.140762, iterations/sec 2.119886, Tokens/sec 227.039828
Iteration 950: training loss 1.068073, iterations/sec 2.523047, Tokens/sec 218.495903
Iteration 960: training loss 1.106662, iterations/sec 2.339293, Tokens/sec 221.063186
Iteration 970: training loss 0.833658, iterations/sec 2.474683, Tokens/sec 213.56517
Iteration 980: training loss 0.844026, iterations/sec 2.441064, Tokens/sec 210.663791
Iteration 990: training loss 0.903735, iterations/sec 2.253876, Tokens/sec 218.175162
Iteration 1000: training loss 0.872615, iterations/sec 2.343899, Tokens/sec 219.62336
Iteration 1000: validation loss 0.714194, validation time 3.470462s
Iteration 1000: saved weights to /tmp/lora-layers-4.safetensors

Testing

You can test the LoRA adapated model against the test dataset using this command:

./mlx-run llm-tool lora test \ 
    --model mlx-community/Mistral-7B-v0.1-hf-4bit-mlx \
    --data Data/lora \
    --adapter /tmp/lora-layers-4.safetensors \
    --batch-size 1 --lora-layers 4 \
    --cache-size 1024

This will run all the items (100 in the example data we are using) in the test set and compute the loss:

Model: mlx-community/Mistral-7B-v0.1-hf-4bit-mlx
Total parameters: 1,242M
Trainable parameters: 0.426M
Test loss 1.327623, ppl 3.772065

Evaluate

Next you can evaluate your own prompts with the fine tuned LoRA adapters. It is important to follow the prompt example from the training data to match the format:

{"text": "table: 1-10015132-1\ncolumns: Player, No., Nationality, Position, Years in Toronto, School/Club Team\nQ: What school did player number 6 come from?\nA: SELECT School/Club Team FROM 1-10015132-1 WHERE No. = '6'"}

Given that format you might issue a command like this:

./mlx-run llm-tool lora eval \
    --model mlx-community/Mistral-7B-v0.1-hf-4bit-mlx \
    --adapter /tmp/lora-layers-4.safetensors \
    --lora-layers 4 \
    --prompt "table: 1-10015132-16
columns: Player, No., Nationality, Position, Years in Toronto, School/Club Team
Q: What is terrence ross' nationality
A: "

Note: the prompt has newlines in it to match the format of the fine tuned prompts -- this may be easier to do with the command line than Xcode.

You might be treated to a response like this:

Model: mlx-community/Mistral-7B-v0.1-hf-4bit-mlx
Total parameters: 1,242M
Trainable parameters: 0.426M
Starting generation ...
table: 1-10015132-16
columns: Player, No., Nationality, Position, Years in Toronto, School/Club Team
Q: What is terrence ross' nationality
A: SELECT Nationality FROM 1-10015132-16 WHERE Player = 'Terrence Ross' AND No. = 1

Fusing

Once the adapter weights are trained you can produce new weights with the original achitecture that have the adapter weights merged in:

./mlx-run llm-tool lora fuse \
    --model mlx-community/Mistral-7B-v0.1-hf-4bit-mlx \
    --adapter /tmp/lora-layers-4.safetensors \
    --output mlx-community/mistral-lora

outputs:

Total parameters: 1,244M
Trainable parameters: 0.426M
Use with:
    llm-tool eval --model mlx-community/mistral-lora

As noted in the output these new weights can be used with the original model architecture.

Footnotes

  1. Refer to the arXiv paper for more details on LoRA. ↩ ↩2

  2. Refer to the paper QLoRA: Efficient Finetuning of Quantized LLMs ↩ ↩2

  3. Refer to the GitHub repo for more information about WikiSQL. ↩