Introduction to Fine-Tuning
Fine-tuning is enhancing a pre-trained language model for specific tasks by training it on targeted examples.
It differs from parameter tuning, which adjusts model settings like temperature or top K.
Fine-tuning is recommended when consistent formatting is needed, when the model lacks domain-specific data, or to reduce costs with smaller models.
Preparing Data for Fine-Tuning
Gathering high-quality data is crucial for effective fine-tuning.
An example dataset used involves HTML extraction with 500 examples of input-output pairs in JSON format.
Fine-tuning can apply to various data types beyond HTML extraction.
Using Unsloth for Training
Unsloth is utilized for its efficiency and speed in fine-tuning LLMs.
Using Google Collaboratory is recommended for accessing powerful GPUs for training.
Connecting to a runtime and setting up the proper environment is necessary for executing the training code.
Setting Up Fine-Tuning
Select an appropriate model to fine-tune based on size and desired performance.
A small model is advised for quicker training times.
Preprocessing data is essential to input it in the required format for training.
Executing the Fine-Tuning Process
The training process involves using a trainer setup to perform the actual fine-tuning.
Training durations can vary based on dataset size and model complexity.
Once training is complete, the model can be set up for inference to ensure it meets expectations.
Integrating the Model with Olama
After training, the model should be saved in a compatible format for Olama.
Creating a model file is necessary to configure and run the new model within Olama.
The final step involves verifying the model's performance through test prompts and ensuring the results are satisfactory.
EASIEST Way to Fine-Tune a LLM and Use It With Ollama
EASIEST Way to Fine-Tune a LLM and Use It With Ollama
Install Browsy