---
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_100
  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_100

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_100 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7824

## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.9444        | 0.1354 | 50   | 0.9377          |
| 0.8668        | 0.2708 | 100  | 0.8816          |
| 0.8436        | 0.4062 | 150  | 0.8559          |
| 0.8192        | 0.5416 | 200  | 0.8384          |
| 0.8285        | 0.6770 | 250  | 0.8261          |
| 0.8132        | 0.8125 | 300  | 0.8165          |
| 0.7997        | 0.9479 | 350  | 0.8076          |
| 0.7335        | 1.0833 | 400  | 0.8050          |
| 0.7466        | 1.2187 | 450  | 0.7997          |
| 0.7264        | 1.3541 | 500  | 0.7957          |
| 0.7286        | 1.4895 | 550  | 0.7911          |
| 0.7251        | 1.6249 | 600  | 0.7876          |
| 0.727         | 1.7603 | 650  | 0.7840          |
| 0.7277        | 1.8957 | 700  | 0.7811          |
| 0.6724        | 2.0311 | 750  | 0.7858          |
| 0.6883        | 2.1666 | 800  | 0.7850          |
| 0.6709        | 2.3020 | 850  | 0.7840          |
| 0.6598        | 2.4374 | 900  | 0.7834          |
| 0.674         | 2.5728 | 950  | 0.7830          |
| 0.656         | 2.7082 | 1000 | 0.7828          |
| 0.6741        | 2.8436 | 1050 | 0.7825          |
| 0.6592        | 2.9790 | 1100 | 0.7824          |


### Framework versions

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