Instructions to use AdnanRiaz107/CodePhi-3-mini-0.06Klora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use AdnanRiaz107/CodePhi-3-mini-0.06Klora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-128k-instruct") model = PeftModel.from_pretrained(base_model, "AdnanRiaz107/CodePhi-3-mini-0.06Klora") - Notebooks
- Google Colab
- Kaggle
metadata
license: mit
library_name: peft
tags:
- generated_from_trainer
base_model: microsoft/Phi-3-mini-128k-instruct
model-index:
- name: CodePhi-3-mini-0.06Klora
results: []
CodePhi-3-mini-0.06Klora
This model is a fine-tuned version of microsoft/Phi-3-mini-128k-instruct on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.7056
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-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 6
- training_steps: 60
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.7307 | 0.1667 | 10 | 0.7108 |
| 0.6895 | 0.3333 | 20 | 0.7088 |
| 0.6816 | 0.5 | 30 | 0.7071 |
| 0.6974 | 0.6667 | 40 | 0.7060 |
| 0.5756 | 0.8333 | 50 | 0.7056 |
| 0.3683 | 1.0 | 60 | 0.7056 |
Framework versions
- PEFT 0.11.0
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1