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---
library_name: peft
license: other
base_model: microsoft/Phi-3-mini-4k-instruct
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: 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. -->
# 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 the flock_task4_tranning dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1386
## 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-05
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- total_eval_batch_size: 2
- 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: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.047 | 0.0501 | 50 | 1.2594 |
| 1.027 | 0.1002 | 100 | 1.2322 |
| 0.916 | 0.1502 | 150 | 1.2325 |
| 0.995 | 0.2003 | 200 | 1.2030 |
| 0.981 | 0.2504 | 250 | 1.1924 |
| 0.9663 | 0.3005 | 300 | 1.1807 |
| 0.8533 | 0.3505 | 350 | 1.1778 |
| 0.9052 | 0.4006 | 400 | 1.1744 |
| 0.9526 | 0.4507 | 450 | 1.1691 |
| 0.8949 | 0.5008 | 500 | 1.1603 |
| 0.8881 | 0.5508 | 550 | 1.1526 |
| 0.9001 | 0.6009 | 600 | 1.1503 |
| 0.8708 | 0.6510 | 650 | 1.1502 |
| 0.8791 | 0.7011 | 700 | 1.1403 |
| 0.9239 | 0.7511 | 750 | 1.1463 |
| 0.8726 | 0.8012 | 800 | 1.1400 |
| 0.8509 | 0.8513 | 850 | 1.1403 |
| 0.8615 | 0.9014 | 900 | 1.1386 |
| 0.9553 | 0.9514 | 950 | 1.1384 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.48.3
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0 |