---
library_name: peft
license: mit
base_model: microsoft/Phi-3.5-mini-instruct
tags:
- base_model:adapter:microsoft/Phi-3.5-mini-instruct
- lora
- transformers
model-index:
- name: phi-3.5-oasst1
  results: []
---

---


# Phi-3.5-OASST1

A fine-tuned version of Phi-3.5 Mini Instruct trained on the OpenAssistant (OASST1) dataset using LoRA and 4-bit quantization. The goal of this project is to improve conversational and instruction-following capabilities while keeping training efficient through parameter-efficient fine-tuning.

## Model Details

### Base Model

* microsoft/Phi-3.5-mini-instruct

### Fine-Tuning Dataset

* OpenAssistant/oasst1

### Training Method

* LoRA (Low-Rank Adaptation)
* 4-bit Quantization using BitsAndBytes
* PEFT (Parameter Efficient Fine-Tuning)

## Training Configuration

| Parameter     | Value                 |
| ------------- | --------------------- |
| Base Model    | Phi-3.5 Mini Instruct |
| Dataset       | OpenAssistant/oasst1  |
| LoRA Rank (r) | 4                     |
| LoRA Alpha    | 32                    |
| Learning Rate | 2e-5                  |
| Quantization  | 4-bit BitsAndBytes    |                   
| Hardware      | NVIDIA T4 GPU         |

## Training Results

Final training metrics:

```text
Train Loss: 2.5009
Global Steps: 4000
Training Runtime: 4507 seconds
Samples per Second: 0.887
Steps per Second: 0.887
Total FLOPs: 4.58e+16
```

## Intended Use

This model is suitable for:

* Conversational AI
* Instruction Following
* Question Answering
* Educational Assistants
* General Text Generation

## Example Usage

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "YOUR_USERNAME/phi-3.5-oasst1"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = "Explain the difference between machine learning and deep learning."

inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(
    **inputs,
    max_new_tokens=128,
    temperature=0.7
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Limitations

* Performance depends on prompt quality.
* Responses should be reviewed before use in critical applications.

## Training Procedure

The model was fine-tuned on OpenAssistant conversations using LoRA adapters with 4-bit quantization to reduce memory usage and training costs. This approach allows efficient adaptation of the base model while retaining most of its original capabilities.

## Acknowledgements

* Microsoft for Phi-3.5 Mini Instruct
* OpenAssistant for the OASST1 dataset
* Hugging Face Transformers
* PEFT
* BitsAndBytes

## Citation

```bibtex
@misc{phi35_oasst1_2026,
  title={Phi-3.5-OASST1},
  author={Abubakar},
  year={2026},
  note={Phi-3.5 Mini Instruct fine-tuned on OpenAssistant OASST1 using LoRA and 4-bit quantization}
}
```


# phi-3.5-oasst1

This model is a fine-tuned version of [microsoft/Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4659


### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.4537        | 1.0   | 4000 | 2.4659          |

language:

* en
  license: mit
  base_model: microsoft/Phi-3.5-mini-instruct
  tags:
* phi-3
* lora
* qlora
* transformers
* conversational-ai
* instruction-tuning
* openassistant
  pipeline_tag: text-generation

---

### Framework versions

- PEFT 0.19.1
- Transformers 5.12.0
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2