testnet-training-node-quickstart

This repository contains a demo script for you to fine-tune a Gemma model for train.flock.io.

Quickstart

Install Dependencies

To set up your environment, run the following commands:

conda create -n training-node python==3.10
conda activate training-node
pip install -r requirements.txt

File Structure

• dataset.py - Contains the logic to process the raw data from demo_data.jsonl.
• demo_data.jsonl - Follows the shareGPT format. The training data you receive from the fed-ledger is in exactly the same format.
• merge.py - Contains the utility function for merging LoRA weights. If you are training with LoRA, please ensure you merge the adapter before uploading to your Hugging Face repository.
• demo.py - A training script that implements LoRA fine-tuning for a Gemma-2B model.
• full_automation.py - A script that automate everything including get a task, download the training data, finetune Gemma-2B on training data, merge weights, upload to your HuggingFace model repo, and submit the task to fed-ledger.
• training_args.yaml - A YAML defines the training hyper-parameters for fine-tuning. A detailed explanation on LoRA config can be found here: LoRA Fine-tuning & Hyperparameters Explained

Full Automation

Simply run

TASK_ID=<task-id> FLOCK_API_KEY="<your-flock-api-key-stakes-as-node-for-the-task>" HF_TOKEN="<your-hf-token>" CUDA_VISIBLE_DEVICES=0 HF_USERNAME="your-hf-user-name" python full_automation.py

The above command will automatically train and submit multiple LLMs that are smaller the max parameters limitation for the given task.

Bypass certain models

If you want to bypass certain models, simply comment out the model config in the training_args.yaml

---

Play with demo.py

Start the Training

Execute the following command to start the training:

HF_TOKEN="hf_yourhftoken" CUDA_VISIBLE_DEVICES=0 python demo.py

The HF token is required due to the Gemma License.

This command initiates fine-tuning on the demo dataset, saves the fine-tuned model, merges the adapter to the base model, and saves the final model.

Upload the model folder to your HuggingFace repo

HuggingFace Models Uploading

Getting the task id

Before you submit the model script for a task, you will first need to stake on the task as a node.

• Go to the FLock Training Platform
• Select Training Node tab and stake on the task you want to submit the model to.
• The task ID is the ID of the task you want to submit the model to.

Submit the model

curl --location 'https://fed-ledger-prod.flock.io/api/v1/tasks/submit-result' \
--header 'flock-api-key: <your-api-key-with-staking-on-this-task-as-node>' \
--header 'Content-Type: application/json' \
--data '{
    "task_id": <task id>,
    "data":{
        "hg_repo_id": "<your-hg-repo-id>",
        "base_model": "gemma"
    }
}'