---
base_model: microsoft/Phi-3-mini-4k-instruct
library_name: peft
license: mit
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: phi-3-mini-LoRA
  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. -->

# phi-3-mini-LoRA

This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8445

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.0374        | 0.0329 | 100  | 1.0194          |
| 0.9715        | 0.0658 | 200  | 0.9314          |
| 0.9106        | 0.0987 | 300  | 0.8969          |
| 0.888         | 0.1316 | 400  | 0.8869          |
| 0.8902        | 0.1645 | 500  | 0.8813          |
| 0.8826        | 0.1974 | 600  | 0.8777          |
| 0.8763        | 0.2303 | 700  | 0.8745          |
| 0.8728        | 0.2632 | 800  | 0.8723          |
| 0.8707        | 0.2961 | 900  | 0.8701          |
| 0.8702        | 0.3289 | 1000 | 0.8684          |
| 0.8631        | 0.3618 | 1100 | 0.8664          |
| 0.8623        | 0.3947 | 1200 | 0.8647          |
| 0.8655        | 0.4276 | 1300 | 0.8624          |
| 0.863         | 0.4605 | 1400 | 0.8602          |
| 0.858         | 0.4934 | 1500 | 0.8586          |
| 0.859         | 0.5263 | 1600 | 0.8578          |
| 0.8527        | 0.5592 | 1700 | 0.8569          |
| 0.8587        | 0.5921 | 1800 | 0.8563          |
| 0.8551        | 0.625  | 1900 | 0.8557          |
| 0.8548        | 0.6579 | 2000 | 0.8550          |
| 0.8515        | 0.6908 | 2100 | 0.8546          |
| 0.8531        | 0.7237 | 2200 | 0.8542          |
| 0.8567        | 0.7566 | 2300 | 0.8535          |
| 0.8589        | 0.7895 | 2400 | 0.8532          |
| 0.8547        | 0.8224 | 2500 | 0.8529          |
| 0.8537        | 0.8553 | 2600 | 0.8525          |
| 0.85          | 0.8882 | 2700 | 0.8521          |
| 0.8518        | 0.9211 | 2800 | 0.8519          |
| 0.8456        | 0.9539 | 2900 | 0.8515          |
| 0.8585        | 0.9868 | 3000 | 0.8512          |
| 0.849         | 1.0197 | 3100 | 0.8509          |
| 0.8549        | 1.0526 | 3200 | 0.8507          |
| 0.8502        | 1.0855 | 3300 | 0.8504          |
| 0.8504        | 1.1184 | 3400 | 0.8502          |
| 0.8488        | 1.1513 | 3500 | 0.8500          |
| 0.8504        | 1.1842 | 3600 | 0.8497          |
| 0.8465        | 1.2171 | 3700 | 0.8495          |
| 0.8471        | 1.25   | 3800 | 0.8494          |
| 0.8467        | 1.2829 | 3900 | 0.8491          |
| 0.8439        | 1.3158 | 4000 | 0.8489          |
| 0.8467        | 1.3487 | 4100 | 0.8487          |
| 0.8461        | 1.3816 | 4200 | 0.8485          |
| 0.8525        | 1.4145 | 4300 | 0.8483          |
| 0.8426        | 1.4474 | 4400 | 0.8481          |
| 0.8479        | 1.4803 | 4500 | 0.8480          |
| 0.853         | 1.5132 | 4600 | 0.8478          |
| 0.8432        | 1.5461 | 4700 | 0.8477          |
| 0.8416        | 1.5789 | 4800 | 0.8475          |
| 0.8527        | 1.6118 | 4900 | 0.8474          |
| 0.849         | 1.6447 | 5000 | 0.8472          |
| 0.8446        | 1.6776 | 5100 | 0.8471          |
| 0.8427        | 1.7105 | 5200 | 0.8469          |
| 0.8464        | 1.7434 | 5300 | 0.8468          |
| 0.8444        | 1.7763 | 5400 | 0.8466          |
| 0.8479        | 1.8092 | 5500 | 0.8465          |
| 0.8452        | 1.8421 | 5600 | 0.8465          |
| 0.8387        | 1.875  | 5700 | 0.8466          |
| 0.845         | 1.9079 | 5800 | 0.8463          |
| 0.8402        | 1.9408 | 5900 | 0.8461          |
| 0.8459        | 1.9737 | 6000 | 0.8460          |
| 0.8431        | 2.0066 | 6100 | 0.8460          |
| 0.8395        | 2.0395 | 6200 | 0.8459          |
| 0.8395        | 2.0724 | 6300 | 0.8458          |
| 0.8457        | 2.1053 | 6400 | 0.8457          |
| 0.8438        | 2.1382 | 6500 | 0.8457          |
| 0.8411        | 2.1711 | 6600 | 0.8456          |
| 0.8386        | 2.2039 | 6700 | 0.8456          |
| 0.8393        | 2.2368 | 6800 | 0.8454          |
| 0.8406        | 2.2697 | 6900 | 0.8454          |
| 0.8386        | 2.3026 | 7000 | 0.8453          |
| 0.8456        | 2.3355 | 7100 | 0.8453          |
| 0.8424        | 2.3684 | 7200 | 0.8452          |
| 0.8437        | 2.4013 | 7300 | 0.8451          |
| 0.8426        | 2.4342 | 7400 | 0.8451          |
| 0.8393        | 2.4671 | 7500 | 0.8450          |
| 0.8398        | 2.5    | 7600 | 0.8450          |
| 0.8434        | 2.5329 | 7700 | 0.8449          |
| 0.8456        | 2.5658 | 7800 | 0.8449          |
| 0.8393        | 2.5987 | 7900 | 0.8449          |
| 0.8401        | 2.6316 | 8000 | 0.8448          |
| 0.838         | 2.6645 | 8100 | 0.8448          |
| 0.8432        | 2.6974 | 8200 | 0.8447          |
| 0.8471        | 2.7303 | 8300 | 0.8447          |
| 0.8435        | 2.7632 | 8400 | 0.8446          |
| 0.8441        | 2.7961 | 8500 | 0.8446          |
| 0.8399        | 2.8289 | 8600 | 0.8446          |
| 0.8391        | 2.8618 | 8700 | 0.8446          |
| 0.8432        | 2.8947 | 8800 | 0.8446          |
| 0.8459        | 2.9276 | 8900 | 0.8446          |
| 0.8446        | 2.9605 | 9000 | 0.8445          |
| 0.8412        | 2.9934 | 9100 | 0.8445          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.43.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1