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library_name: peft
license: mit
base_model: microsoft/phi-2
tags:
- base_model:adapter:microsoft/phi-2
- lora
- transformers
pipeline_tag: text-generation
model-index:
- name: phi-2-QLoRa2-Python
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-2-QLoRa2-Python
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.3289
## 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 4.391 | 0.1333 | 100 | 4.3380 |
| 4.273 | 0.2667 | 200 | 4.3430 |
| 4.1309 | 0.4 | 300 | 4.3425 |
| 4.2583 | 0.5333 | 400 | 4.3461 |
| 4.2424 | 0.6667 | 500 | 4.3353 |
| 3.9205 | 0.8 | 600 | 4.3342 |
| 4.0227 | 0.9333 | 700 | 4.3312 |
| 4.3798 | 1.0667 | 800 | 4.3311 |
| 4.3908 | 1.2 | 900 | 4.3360 |
| 3.8848 | 1.3333 | 1000 | 4.3354 |
| 4.3534 | 1.4667 | 1100 | 4.3325 |
| 3.937 | 1.6 | 1200 | 4.3335 |
| 3.9995 | 1.7333 | 1300 | 4.3323 |
| 4.3121 | 1.8667 | 1400 | 4.3314 |
| 4.5389 | 2.0 | 1500 | 4.3312 |
| 3.8369 | 2.1333 | 1600 | 4.3316 |
| 4.3627 | 2.2667 | 1700 | 4.3309 |
| 3.6879 | 2.4 | 1800 | 4.3305 |
| 4.4777 | 2.5333 | 1900 | 4.3296 |
| 4.2731 | 2.6667 | 2000 | 4.3293 |
| 4.1431 | 2.8 | 2100 | 4.3291 |
| 4.1535 | 2.9333 | 2200 | 4.3289 |
### Framework versions
- PEFT 0.17.1
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0 |