---
language: en
tags:
  - llama
  - fine-tuning
  - text-generation
  - causal-lm
  - NLP
license: mit
datasets:
  - custom-dataset-100k
---

# Llama-3.2-3b-FineTome-100k


## Model Description

**Llama-3.2-3b-FineTome-100k** is a fine-tuned version of the Llama 3.2 model, optimized for various natural language processing (NLP) tasks. It has been trained on a dataset containing 100,000 examples, designed to improve its performance on domain-specific applications.

### Key Features

- **Model Size**: 3 billion parameters
- **Architecture**: Transformer-based architecture optimized for NLP tasks
- **Fine-tuning Dataset**: 100k curated examples from diverse sources

## Use Cases

- Text generation
- Sentiment analysis
- Question answering
- Language translation
- Dialogue systems

## Installation

To use the **Llama-3.2-3b-FineTome-100k** model, ensure you have the `transformers` library installed. You can install it using pip:

```bash
pip install transformers

from transformers import AutoTokenizer, AutoModelForCausalLM

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("huggingface/llama-3.2-3b-finetome-100k")
model = AutoModelForCausalLM.from_pretrained("huggingface/llama-3.2-3b-finetome-100k")

# Encode input text
input_text = "What are the benefits of using Llama-3.2-3b-FineTome-100k?"
input_ids = tokenizer.encode(input_text, return_tensors='pt')

# Generate output
output = model.generate(input_ids, max_length=50)
output_text = tokenizer.decode(output[0], skip_special_tokens=True)

print(output_text)
```