---
library_name: transformers
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: all_tasks_combined_8b_sft
  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. -->

# all_tasks_combined_8b_sft

This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the identity and the data_mc_filtered datasets.
It achieves the following results on the evaluation set:
- Loss: 0.4943

## 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-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.4639        | 0.0929 | 50   | 0.5398          |
| 0.4939        | 0.1857 | 100  | 0.5122          |
| 0.4822        | 0.2786 | 150  | 0.5242          |
| 0.4701        | 0.3714 | 200  | 0.5521          |
| 0.4216        | 0.4643 | 250  | 0.5374          |
| 0.4159        | 0.5571 | 300  | 0.5146          |
| 0.4502        | 0.6500 | 350  | 0.5022          |
| 0.4625        | 0.7428 | 400  | 0.4985          |
| 0.4313        | 0.8357 | 450  | 0.4716          |
| 0.4472        | 0.9285 | 500  | 0.4771          |
| 0.2753        | 1.0204 | 550  | 0.5026          |
| 0.2877        | 1.1133 | 600  | 0.4784          |
| 0.3038        | 1.2061 | 650  | 0.4795          |
| 0.2944        | 1.2990 | 700  | 0.4682          |
| 0.2722        | 1.3918 | 750  | 0.4681          |
| 0.2734        | 1.4847 | 800  | 0.4480          |
| 0.2826        | 1.5775 | 850  | 0.4484          |
| 0.2344        | 1.6704 | 900  | 0.4388          |
| 0.2437        | 1.7632 | 950  | 0.4272          |
| 0.2113        | 1.8561 | 1000 | 0.4233          |
| 0.2548        | 1.9489 | 1050 | 0.4117          |
| 0.1126        | 2.0409 | 1100 | 0.5031          |
| 0.1128        | 2.1337 | 1150 | 0.4821          |
| 0.0993        | 2.2266 | 1200 | 0.4997          |
| 0.0978        | 2.3194 | 1250 | 0.4896          |
| 0.1056        | 2.4123 | 1300 | 0.4980          |
| 0.0897        | 2.5051 | 1350 | 0.4883          |
| 0.0872        | 2.5980 | 1400 | 0.4941          |
| 0.0916        | 2.6908 | 1450 | 0.4939          |
| 0.0844        | 2.7837 | 1500 | 0.4945          |
| 0.0959        | 2.8765 | 1550 | 0.4943          |
| 0.094         | 2.9694 | 1600 | 0.4941          |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0