---
library_name: peft
license: mit
base_model: microsoft/Phi-3-medium-128k-instruct
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Adapter-Phi-3-medium-128k-instruct-lora-hrdx-gptq
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Adapter-Phi-3-medium-128k-instruct-lora-hrdx-gptq

This model is a fine-tuned version of [microsoft/Phi-3-medium-128k-instruct](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0599

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log        | 1.4023 | 30   | 2.3764          |
| No log        | 2.8046 | 60   | 2.1774          |
| No log        | 4.2299 | 90   | 2.0599          |


### Framework versions

- PEFT 0.13.2
- Transformers 4.46.2
- Pytorch 2.4.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3