---
library_name: peft
license: other
base_model: ibm-granite/granite-3.3-8b-instruct
tags:
- llama-factory
- lora
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: factory_granite_results
  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. -->

# factory_granite_results

This model is a fine-tuned version of [ibm-granite/granite-3.3-8b-instruct](https://huggingface.co/ibm-granite/granite-3.3-8b-instruct) on the train dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2523
- Accuracy: 0.9475

## 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: 0.0001
- 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 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: 0.03
- num_epochs: 9.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4051        | 1.0   | 32   | 0.4305          | 0.8978   |
| 0.2886        | 2.0   | 64   | 0.3232          | 0.9206   |
| 0.2289        | 3.0   | 96   | 0.2742          | 0.9323   |
| 0.1925        | 4.0   | 128  | 0.2514          | 0.9387   |
| 0.1079        | 5.0   | 160  | 0.2456          | 0.9420   |
| 0.0968        | 6.0   | 192  | 0.2410          | 0.9454   |
| 0.0835        | 7.0   | 224  | 0.2464          | 0.9466   |
| 0.0716        | 8.0   | 256  | 0.2516          | 0.9472   |
| 0.0611        | 9.0   | 288  | 0.2523          | 0.9475   |


### Framework versions

- PEFT 0.15.2
- Transformers 4.52.4
- Pytorch 2.7.0
- Datasets 3.6.0
- Tokenizers 0.21.1