transformers safetensors llama text-generation text-generation-inference trl meta summary summarization en dataset:openai/summarize_from_feedback base_model:meta-llama/Llama-3.2-1B-Instruct base_model:finetune:meta-llama/Llama-3.2-1B-Instruct license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
base_model:
- meta-llama/Llama-3.2-1B-Instruct
datasets:
- openai/summarize_from_feedback
language:
- en
license: apache-2.0
metrics:
- accuracy
tags:
- text-generation-inference
- transformers
- llama
- trl
- meta
- summary
- summarization
---

# 🌟 Summarization Model Card 🌟

## Model Overview

- **Model Name:** Llama-3.2-1B Instruct Model Fine-tuned for Summarization
- **Developed by:** [saishshinde15](https://huggingface.co/saishshinde15)
- **License:** Apache-2.0
- **Base Model:** [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct)

## Description

This model has been fine-tuned to excel in generating concise and informative summaries from lengthy texts. It captures key ideas while presenting them in an easy-to-read bullet-point format.

### Key Features

- **Language:** English
- **Fine-tuned on:** The dataset `openai/summarize_from_feedback` for improved summarization capabilities.
- **Performance Metric:** Evaluated based on accuracy.

## Prompt for Optimal Use

For the best results, please utilize the following tried-and-true prompt structure:

```plaintext
You are given the following text. Please provide a summary in 5-10 key points, depending on the length of the document. Each point should be clearly formatted in bullet format, starting with an asterisk (*).

**Note:** The examples provided below are for your reference only and should not be included in your response.

### Examples (for reference only):
* The sky is blue on a clear day.
* Water boils at 100 degrees Celsius.
* Trees produce oxygen through photosynthesis.

### Original Text:
{}

### Key Points Summary (in bullet points):



# Model Loading Instructions

To load this model, use the following code snippet:

```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer

# Replace "lora_model" with your actual model name
model = AutoPeftModelForCausalLM.from_pretrained(
    "saishshinde15/Summmary_Model_Llama-3.2-1B-Instruct",  # YOUR MODEL YOU USED FOR TRAINING
    load_in_4bit=True,  # Adjust as needed
)
tokenizer = AutoTokenizer.from_pretrained("saishshinde15/Summmary_Model_Llama-3.2-1B-Instruct")