This no-frills guide will take you from a dataset to a fine-tuned Llama model in the matter of minutes.

This repository is easy to tweak but comes ready to use as-is with all the recommended, start-of-the-art optimizations for fast results:

• Fully-sharded data parallelism so training scales optimally with multi-GPU
• Parameter-efficient fine-tuning via LoRa adapters for faster convergence
• Gradient checkpointing to reduce VRAM footprint, fit larger batches and get higher training throughput

The heavy lifting is done by the our fork of the llama-recipes library (original here). Our fork patches support for Code Llama and an open issue causing CUDA OOMs while saving LORA state dicts for 70B models.

Best of all, using Modal for fine-tuning means you never have to worry about infrastructure headaches like building images and provisioning GPUs. If a training script runs on Modal, it's repeatable and scalable enough to ship to production right away.

1. Create a Modal account.
2. Install modal in your current Python virtual environment (pip install modal)
3. Set up a Modal token in your environment (modal token new)

To launch a training job, use:

modal run train.py --dataset sql_dataset.py --base chat7 --run-id chat7-sql

This example fine-tunes Llama 7B Chat to produce SQL queries (10k examples trained for 10 epochs in about 30 minutes). The base model nicknames used can be configured in common.py.

Next, run inference to compare the results before/after training:

modal run inference.py --base chat7 --run-id chat7-sql --prompt '[INST] <<SYS>>
You are an advanced SQL assistant that uses this SQL table schema to generate a SQL query which answers the user question.
CREATE TABLE table_name_66 (points INTEGER, against VARCHAR, played VARCHAR)
<</SYS>>

What is the sum of Points when the against is less than 24 and played is less than 20? [/INST]'

Add --batch 10000 to scale up seamlessly to dozens of GPUs for effortless parallelism as we complete 10000 prompts.

Follow the example set by sql_dataset.py or local_dataset.py to import your own dataset. Then use

modal run validate_dataset.py --dataset new_dataset.py --base chat7 

to that validate your new script produces the desired training and test sets.

Tip: ensure your training set is large enough to run a single step (i.e. contains at least N_GPUS * batch_size rows) to avoid torch errors.