---
license: apache-2.0
tags:
- unsloth
- trl
- sft
datasets:
- billa-man/llm-prompt-recovery
language:
- en
base_model:
- unsloth/Llama-3.2-3B-Instruct
pipeline_tag: text-generation
library_name: transformers
---

Model that I used for this Kaggle Competition: [LLM Prompt Recovery](https://www.kaggle.com/competitions/llm-prompt-recovery)

My Kaggle implementation: [Notebook](https://www.kaggle.com/code/sohithbandari/llama-3-2-3b-llm-prompt-recovery)

## Usage:
```
from unsloth import FastLanguageModel
from peft import PeftModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Llama-3.2-3B-Instruct",
    max_seq_length = 512,
    dtype = None,
    load_in_4bit = True,
)

model = PeftModel.from_pretrained(model, "billa-man/llm-prompt-recovery")
```

## Input to the LLM is in the following format:

```
{"role": "user", "content": "Return the prompt that was used to tranform the original text into the rewritten text. Original Text: " + original_text +", Rewritten Text: " + rewritten_text}
```

## An example:
```
original_text = "Recent breakthroughs have demonstrated several ways to induce magnetism in materials using light, with significant implications for future computing and data storage technologies."
rewritten_text = "Light-induced magnetic phase transitions in non-magnetic materials have been experimentally demonstrated through ultrafast optical excitation, offering promising pathways for photomagnetic control in next-generation spintronic devices and quantum computing architectures."

inference(original_text, rewritten_text)  (check notebook for code)
```
```
> 'Rewrite the following sentence while maintaining its original meaning but using different wording and terminology: "Recent breakthroughs have demonstrated several ways to induce magnetism in materials using light, with significant implications for future computing and data storage technologies."'
```