Finetuning LLMs with Wisp #

Finetuning LLMs usually requires large GPUs, which Wisp is ideal for finding and configuring for you.

In this example, we will finetune a LLM on a Wisp instance, and download the model on completion.

The LLM #

We’ll use mistral-finetune to finetune a Mistral-7B model. If you need to finetune other models, you can also run your own code with Wisp.

The steps in this guide is taken directly from the Readme of mistral-finetune.

Create your Project #

First, we need to follow the instructions from mistral-finetune to set up the project locally before running remotely.

First, clone the repository:

cd $HOME && git clone <https://github.com/mistralai/mistral-finetune.git>

Edit example/7B.yaml so that data, model and run_dir looks like this:

data:
    instruct_data: "~/data/ultrachat_chunk_train.jsonl"
    eval_instruct_data: "~/ultrachat_chunk_eval.jsonl"

model_id_or_path: "~/"mistral_models/7B""

run_dir: "~/mistral-finetune"
...

Prepare the Data #

Following the Readme from mistral-finetune, we’ll finetune the model with UltraChat_200k for chat bot functionality.

cd $HOME && mkdir -p data && cd $HOME/data

Install dependencies for dataset modification:

pip install pandas pyarrow

In a Python shell, run

import pandas as pd

df = pd.read_parquet('<https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k/resolve/main/data/test_gen-00000-of-00001-3d4cd8309148a71f.parquet>')

# Split into train and eval

df_train=df.sample(frac=0.95,random_state=200)
df_eval=df.drop(df_train.index)

# Save to jsonl

df_train.to_json("ultrachat_chunk_train.jsonl", orient="records", lines=True)
df_eval.to_json("ultrachat_chunk_eval.jsonl", orient="records", lines=True)

Optionally, you can reformat and validate the data using the instructions in the Readme.

Configuration #

If you haven’t done yet, in the cloned repository’s root, run wisp init to create the configuration file. Open wisp-config.yml and enter the following information:

setup:
    project: local
    remote: <https://github.com/mistralai/mistral-finetune>

    script:
        pip install -r requirements.txt
        mkdir -p ~/${HOME}/mistral_models
        cd ${HOME} && wget <https://models.mistralcdn.com/mistral-7b-v0-3/mistral-7B-v0.3.tar>
        tar -xf mistral-7B-v0.3.tar -C mistral_models

io:
    input:
        volume: "~/data:~/data"
        type: copy
    output:
        volume: "~/mistral-output:~/mistral-output"
        type: copy

run:
    cd $HOME/mistral-finetune
    torchrun --nproc-per-node 8 --master_port $RANDOM -m train example/7B.yaml

resources:
    accelerators:
        name: H100
        count: 8

Let’s go through this configuration from the beginning.

setup #

Defines that the project is local, meaning the whole working directory (excluding gitignore files) will be copied to the cloud instance when running. When setting up the node, the script phase will install requirements and download the model to the server.

io #

The ìnput step mounts the local ~/data folder to the remote ~/data folder using copy. That means that the data we downloaded and split up locally will be copied directly to the node’s folder at the same directory.

Similarly, the outputstep will download any outputs from the remote’s "~/mistral-output folder to the local machine one the job is done.

You can also specify a remote data store, for example a s3 bucket, if you don’t want to download the model locally.

run #

This step simply runs the training script.

resources #

This step sets the constraints for the server. Mistral recommends using 8xH100 for finetuning (this should only take 30 minutes), but you can use any number of nodes, or even smaller GPU’s if you want.

Teardown #

Once done, you can tear down the instance with

wisp destroy

If you want to keep the persistent data to avoid setting up the instance next time you need to run it, simply run

wisp pause

Note that you will still be charged for some resources when pausing instances. The command above will give you a detailed cost breakdown before pausing.

You can see your job, stats and cost overview for your job in the dashboard under Jobs.