giotto-llm 0.0.1

Wrapper of many OS libraries to finetune LLMs and run them in inference mode efficiently.

giotto-llm

Overview

This repo is a wrapper of many open source packages that are needed to fine-tune LLMs and run them in inference mode effectively.

Important: See the Makefile for useful commands.

Documentation

The online documentation con be found at https://giotto-ai.github.io/giotto-llm/index.html

Setup

Environmental variables

The following environmental variables needs to be set (update credential path below):

GOOGLE_APPLICATION_CREDENTIALS=$HOME/.gloud/giotto-research-admin.json
export MLFLOW_TRACKING_URI=http://cluster-manager:5051
export PATH=$HOME/.local/bin:$PATH

Gitlab ssh-key

Generate key with ssh-keygen, and add to gitlab.

Install and download packages, download data and model

make

Finetune models on multi-gpu

Example of QwenVL-based model:

torchrun --nproc-per-node=gpu -m llm_prompts.finetuning -d re_arc_400x5 --model_id Qwen/Qwen2-VL-2B-Instruct --wrapper QwenVL -o qwenvl --batch_size 1 --gradient_accumulation_steps 16 --quantization 8bit-4 --neftune_noise_alpha 10.0 --num_train_epochs 15 --learning_rate 2e-4

Example of Molmo-based model:

torchrun --nproc-per-node=gpu -m llm_prompts.finetuning -d re_arc_400x5 --model_id allenai/MolmoE-1B-0924 --wrapper Molmo -o molmo --batch_size 1 --gradient_accumulation_steps 16 --quantization 8bit-4 --neftune_noise_alpha 10.0 --num_train_epochs 15 --learning_rate 2e-4

Example of Llama-based models:

torchrun --nproc-per-node=gpu -m llm_prompts.finetuning -d re_arc_400x5 --model_id meta-llama/Llama-3.2-1B-Instruct --wrapper CausalLM -o llama --batch_size 1 --gradient_accumulation_steps 16 --quantization 8bit-4 --neftune_noise_alpha 10.0 --num_train_epochs 15 --learning_rate 2e-4

Example of Qwen-based models:

torchrun --nproc-per-node=gpu -m llm_prompts.finetuning -d re_arc_400x5 --model_id Qwen/Qwen2.5-0.5B --wrapper CausalLM -o qwen --batch_size 1 --gradient_accumulation_steps 16 --quantization 8bit-4 --neftune_noise_alpha 10.0 --num_train_epochs 15 --learning_rate 2e-4

See full list of currently tested models in ./models/.

Validate fine-tuned models

The validation script is single gpu for now, and requires a config entry in ./llm_prompts/validation/__main__.py.

# Only single gpu support for now
CUDA_VISIBLE_DEVICES=0 python -m llm_prompts.validation --dataset_type evaluation --finetuned_model_id <MODEL-ID> --max_num_tasks 400

where <MODEL-ID> is a fine-tuned model defined in the config.

Info

See

• ARC technical guide: specs

--- CPU only ---
4 CPU Cores
30 Gigabytes of RAM

or

--- P100 GPU ---
1 Nvidia Tesla P100 GPI
4 CPU cores
29 Gigabytes of RAM

or

--- T4 2x GPU ---
2 Nvidia Tesla T4 GPUs
4 CPU cores
29 Gigabytes of RAM

• Awesome ARC: lots of information
  • LLMs as a System of Multiple Expert Agents to solve the ARC Challenge (Detailed Walkthrough)
  • LLMs as a system to solve the Abstraction and Reasoning Corpus (ARC) Challenge!
• Lots of data: millions of synthetically generated tasks
• Unsloth: fine-tune LLMs. Also with quantization.
• Fine-tuning example: how to fine-tune and quantize a LLM model from HugginFace

Notes

• The current version of this repository is using version 4.43.2 of the transformer package, which is different from version 4.42.3 in the Kaggle environemnt.
• The current version of this repository is using version 1.7.1 of the polars package, which is different from version 1.1.0 in the Kaggle environemnt.