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:
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)