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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_task5_tranning dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3370
## 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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 4
- 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: 0.05
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.6968 | 0.4255 | 5 | 1.6353 |
| 1.4036 | 0.8511 | 10 | 1.5311 |
| 1.5081 | 1.3404 | 15 | 1.4970 |
| 1.4796 | 1.7660 | 20 | 1.4684 |
| 1.5587 | 2.2553 | 25 | 1.4455 |
| 1.2971 | 2.6809 | 30 | 1.4280 |
| 1.465 | 3.1702 | 35 | 1.4097 |
| 1.3267 | 3.5957 | 40 | 1.3954 |
| 1.3003 | 4.0851 | 45 | 1.3800 |
| 1.1868 | 4.5106 | 50 | 1.3672 |
| 1.3354 | 4.9362 | 55 | 1.3562 |
| 1.1273 | 5.4255 | 60 | 1.3492 |
| 1.2471 | 5.8511 | 65 | 1.3439 |
| 1.1704 | 6.3404 | 70 | 1.3409 |
| 1.1422 | 6.7660 | 75 | 1.3381 |
| 1.2064 | 7.2553 | 80 | 1.3379 |
| 1.2972 | 7.6809 | 85 | 1.3370 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.48.3
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0 |