File size: 1,578 Bytes
37e39e6 df226c4 37e39e6 2e4323a 37e39e6 19d4973 62d4a3e 19d4973 62d4a3e 19d4973 e4f58f1 19d4973 0139cb6 19d4973 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 |
---
language: en
tags:
- llama
- Peft
- fine-tuning
- text-generation
- causal-lm
- NLP
license: mit
datasets:
- mlabonne/FineTome-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
```
```bash
from transformers import AutoTokenizer, AutoModelForCausalLM
# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("khushwant04/Llama-3.2-3b-FineTome-100k")
model = AutoModelForCausalLM.from_pretrained("khushwant04/Llama-3.2-3b-FineTome-100k")
# Encode input text
input_text = "Tell me someting intresting about India and its culture?"
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)
``` |