transformers safetensors physics lora Qlora phi-2 text-generation en dataset:ayoubkirouane/arxiv-physics endpoints_compatible region:us
---
language:
- en
library_name: transformers
tags:
- physics
- lora
- Qlora
- phi-2
datasets:
- ayoubkirouane/arxiv-physics
pipeline_tag: text-generation
---


## Phi-2-arxiv-physics  : 

Phi-2-arxiv-physics is a small language model (SLM) derived from Microsoft Research's Phi-2, a Transformer-based model with 2.7 billion parameters. 
Specifically fine-tuned using the innovative QLoRA (Quantized Low-rank Adapters) technique, this model exhibits remarkable capabilities in the domain of physics-related text.




## Fine-Tuning:

The model has been fine-tuned using **QLoRA**, a technique that reduces computational and memory requirements during the fine-tuning process. 

This two-stage approach involves quantizing parameters and introducing learnable low-rank adapters, resulting in faster fine-tuning with reduced memory demands.

## Data Source:

**Phi-2-arxiv-physics** is specifically trained on the **arXiv-physics** dataset, consisting of questions and answers related to physics. 

This dataset is sourced from the arXiv repository, encompassing a wide range of topics within the field of physics.


Usage (High-level) : 
```
!pip install -q -U git+https://github.com/huggingface/transformers.git

```

``` python

from transformers import pipeline

pipe = pipeline("text-generation", model="ayoubkirouane/phi-2-arxiv-physics")

## or :

# load it directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ayoubkirouane/phi-2-arxiv-physics")

```


