---
license: mit
language:
- en
base_model:
- microsoft/Phi-4-multimodal-instruct
---

# Galactus
This model is a fine-tuned version of microsoft/Phi-4-multimodal-instruct on the Galaxy's Last Exam Benchmark.

<p align="center">
  <img width="50%" src="Main_Image.png">
</p>


## Model description
Galactus is a SOTA multimodal language model that outperforms all OpenAI and Gemini models on the Galaxy's Last Exam Benchmark. 
This benchmark features challenging tasks that push the boundaries of metaphysical competence—for instance, determining how many times two lines intersect or simulating the effect of adding three minutes to an analog clock.
The model accepts image input along with text prompts and has been specifically optimized to tackle the most complex visual reasoning tasks.

## Intended uses & limitations
This model is intended for handling complex visual reasoning tasks that require metaphysical competence.
Please do not use for normal human tasks. 

## Training and evaluation data
The model was exclusively trained on the Galaxy's Last Exam Benchmark.

## Training procedure
The model was trained using LoRA adapters focused on the vision components of the base model.

## Prompt format
This model uses the following image prompt format:
<|image_1|> + user text

## Training hyperparameters
The following hyperparameters were used during training:

num_train_epochs: specified in args (but used checkpoint @ 252 epochs)
per_device_train_batch_size: specified in args
gradient_checkpointing: True
gradient_checkpointing_kwargs: {'use_reentrant': False}
gradient_accumulation_steps: specified in args
optim: 'adamw_torch'
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-7
learning_rate: specified in args
weight_decay: 0.0
save_strategy: 'steps'
save_steps: 10
eval_steps: 10 if eval_dataset else None
evaluation_strategy: 'steps' if eval_dataset else 'no'
load_best_model_at_end: True if eval_dataset else False
max_grad_norm: 1.0
lr_scheduler_type: 'linear'
warmup_steps: 50
logging_steps: 10
save_total_limit: 2
save_only_model: True
dataloader_num_workers: 4
ddp_find_unused_parameters: True

## Training results
The model achieved 72% performance on the Galaxy's Last Exam Benchmark.
https://github.com/DavidTee1/Galaxys-Last-Exam-Benchmark

## Framework versions

Transformers 4.46.1
PyTorch 2.7.0.dev20250304+cu128
TorchVision 0.22.0.dev20250304+cu128
Tokenizers 0.20.3