---
base_model: microsoft/Phi-3-mini-128k-instruct
library_name: peft
license: mit
tags:
- llama-factory
- lora
- full
- generated_from_trainer
model-index:
- name: PairRM-V2-phi3-3-mini-ultra-feedback-binarized-lora
  results: []
---

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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/dongfu/huggingface/runs/vdittf2b)
# PairRM-V2-phi3-3-mini-ultra-feedback-binarized-lora

This model is a fine-tuned version of [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co./microsoft/Phi-3-mini-128k-instruct) on the ultra-feedback-binarized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2605

## 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: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.2858        | 0.6406 | 500  | 0.2640          |


### Framework versions

- PEFT 0.11.1
- Transformers 4.43.1
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1