---
base_model: microsoft/Phi-3.5-mini-instruct
library_name: peft
license: mit
tags:
- generated_from_trainer
model-index:
- name: phi3_5_mini_instruct_lora_chemical_eng_flash
  results: []
---

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# phi3_5_mini_instruct_lora_chemical_eng_flash

This model is a fine-tuned version of [microsoft/Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0794

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0874        | 0.7319 | 100  | 0.0869          |
| 0.0781        | 1.4639 | 200  | 0.0809          |
| 0.0764        | 2.1958 | 300  | 0.0800          |
| 0.0782        | 2.9277 | 400  | 0.0794          |
| 0.0769        | 3.6597 | 500  | 0.0795          |
| 0.0697        | 4.3916 | 600  | 0.0794          |


### Framework versions

- PEFT 0.13.2
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1