---
library_name: transformers
license: other
base_model: llava-hf/llava-v1.6-mistral-7b-hf
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: CompCap-GPT4
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# CompCap-GPT4

This model is a fine-tuned version of [llava-hf/llava-v1.6-mistral-7b-hf](https://huggingface.co/llava-hf/llava-v1.6-mistral-7b-hf) on the CompCap-GPT4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7652

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 3.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.9184        | 0.1296 | 50   | 0.9209          |
| 0.8611        | 0.2592 | 100  | 0.8653          |
| 0.8257        | 0.3889 | 150  | 0.8396          |
| 0.8308        | 0.5185 | 200  | 0.8231          |
| 0.8168        | 0.6481 | 250  | 0.8109          |
| 0.8021        | 0.7777 | 300  | 0.8005          |
| 0.7741        | 0.9073 | 350  | 0.7929          |
| 0.7155        | 1.0369 | 400  | 0.7880          |
| 0.7322        | 1.1666 | 450  | 0.7837          |
| 0.7214        | 1.2962 | 500  | 0.7790          |
| 0.6936        | 1.4258 | 550  | 0.7753          |
| 0.7046        | 1.5554 | 600  | 0.7717          |
| 0.6967        | 1.6850 | 650  | 0.7690          |
| 0.7197        | 1.8146 | 700  | 0.7658          |
| 0.704         | 1.9443 | 750  | 0.7633          |
| 0.6546        | 2.0739 | 800  | 0.7677          |
| 0.651         | 2.2035 | 850  | 0.7673          |
| 0.6601        | 2.3331 | 900  | 0.7667          |
| 0.6669        | 2.4627 | 950  | 0.7662          |
| 0.6566        | 2.5924 | 1000 | 0.7657          |
| 0.6654        | 2.7220 | 1050 | 0.7655          |
| 0.6389        | 2.8516 | 1100 | 0.7653          |
| 0.6574        | 2.9812 | 1150 | 0.7653          |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.20.3