---
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_l0_0_80
  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_l0_0_80

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_l0_0_80 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7854

## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.9459        | 0.1692 | 50   | 0.9275          |
| 0.8992        | 0.3384 | 100  | 0.8709          |
| 0.8515        | 0.5076 | 150  | 0.8446          |
| 0.8498        | 0.6768 | 200  | 0.8278          |
| 0.8105        | 0.8460 | 250  | 0.8158          |
| 0.7739        | 1.0152 | 300  | 0.8077          |
| 0.7286        | 1.1844 | 350  | 0.8026          |
| 0.7582        | 1.3536 | 400  | 0.7977          |
| 0.7386        | 1.5228 | 450  | 0.7925          |
| 0.7479        | 1.6920 | 500  | 0.7887          |
| 0.7374        | 1.8613 | 550  | 0.7846          |
| 0.7075        | 2.0305 | 600  | 0.7871          |
| 0.6818        | 2.1997 | 650  | 0.7874          |
| 0.6747        | 2.3689 | 700  | 0.7868          |
| 0.6849        | 2.5381 | 750  | 0.7861          |
| 0.6922        | 2.7073 | 800  | 0.7856          |
| 0.6948        | 2.8765 | 850  | 0.7854          |


### Framework versions

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