---
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_cooccur_0_70
  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_cooccur_0_70

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_cooccur_0_70 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8001

## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.9216        | 0.1934 | 50   | 0.9377          |
| 0.872         | 0.3868 | 100  | 0.8799          |
| 0.8452        | 0.5803 | 150  | 0.8544          |
| 0.8444        | 0.7737 | 200  | 0.8375          |
| 0.8236        | 0.9671 | 250  | 0.8248          |
| 0.776         | 1.1605 | 300  | 0.8194          |
| 0.7598        | 1.3540 | 350  | 0.8137          |
| 0.7539        | 1.5474 | 400  | 0.8075          |
| 0.7273        | 1.7408 | 450  | 0.8021          |
| 0.7314        | 1.9342 | 500  | 0.7983          |
| 0.7094        | 2.1277 | 550  | 0.8029          |
| 0.7073        | 2.3211 | 600  | 0.8018          |
| 0.6944        | 2.5145 | 650  | 0.8011          |
| 0.6841        | 2.7079 | 700  | 0.8003          |
| 0.6832        | 2.9014 | 750  | 0.8001          |


### Framework versions

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