---
library_name: transformers
license: other
base_model: meta-llama/Llama-3.2-3B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: t1_25k_v2_tag5
  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. -->

# t1_25k_v2_tag5

This model is a fine-tuned version of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) on the t1_25k_v2_tag5 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3038

## 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: 5e-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 8
- total_eval_batch_size: 8
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.3537        | 0.0833 | 100  | 0.4035          |
| 0.3871        | 0.1667 | 200  | 0.3606          |
| 0.321         | 0.25   | 300  | 0.3481          |
| 0.3558        | 0.3333 | 400  | 0.3372          |
| 0.3775        | 0.4167 | 500  | 0.3321          |
| 0.3283        | 0.5    | 600  | 0.3225          |
| 0.3371        | 0.5833 | 700  | 0.3186          |
| 0.3005        | 0.6667 | 800  | 0.3113          |
| 0.3223        | 0.75   | 900  | 0.3080          |
| 0.3302        | 0.8333 | 1000 | 0.3047          |
| 0.2852        | 0.9167 | 1100 | 0.3041          |
| 0.2686        | 1.0    | 1200 | 0.3038          |


### Framework versions

- Transformers 4.46.1
- Pytorch 2.6.0+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3