---
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_90
  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_90

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_90 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7879

## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.9273        | 0.1505 | 50   | 0.9361          |
| 0.8938        | 0.3010 | 100  | 0.8805          |
| 0.8476        | 0.4515 | 150  | 0.8552          |
| 0.8413        | 0.6020 | 200  | 0.8378          |
| 0.8254        | 0.7524 | 250  | 0.8255          |
| 0.8091        | 0.9029 | 300  | 0.8149          |
| 0.7271        | 1.0534 | 350  | 0.8105          |
| 0.7383        | 1.2039 | 400  | 0.8056          |
| 0.7543        | 1.3544 | 450  | 0.8005          |
| 0.7373        | 1.5049 | 500  | 0.7961          |
| 0.7145        | 1.6554 | 550  | 0.7924          |
| 0.7176        | 1.8059 | 600  | 0.7887          |
| 0.7384        | 1.9564 | 650  | 0.7858          |
| 0.6877        | 2.1068 | 700  | 0.7907          |
| 0.6796        | 2.2573 | 750  | 0.7899          |
| 0.6837        | 2.4078 | 800  | 0.7888          |
| 0.6653        | 2.5583 | 850  | 0.7885          |
| 0.6563        | 2.7088 | 900  | 0.7879          |
| 0.6829        | 2.8593 | 950  | 0.7879          |


### Framework versions

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