---
base_model: unsloth/Llama-3.2-3B-Instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---

# Uploaded  model

- **Developed by:** iFaz
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-3B-Instruct-bnb-4bit
# Model Card: `unsloth/Llama-3.2-3B-Instruct-bnb-4bit`

## Overview  
This is a fine-tuned version of the `unsloth/Llama-3.2-3B-Instruct-bnb-4bit` model, optimized for instruction-following tasks. The model leverages the efficiency of 4-bit quantization, making it lightweight and resource-efficient while maintaining high-quality outputs. It is particularly suited for text generation tasks in English, with applications ranging from conversational AI to natural language understanding tasks.

## Key Features  
- **Base Model:** `unsloth/Llama-3.2-3B`  
- **Quantization:** Utilizes 4-bit precision, enabling deployment on resource-constrained systems while maintaining performance.  
- **Language:** English-focused, with robust generalization capabilities across diverse text-generation tasks.  
- **Fine-Tuning:** Enhanced for instruction-following tasks to generate coherent and contextually relevant responses.  
- **Versatile Applications:** Ideal for text generation, summarization, dialogue systems, and other natural language processing (NLP) tasks.  

## Model Details  
- **Developer:** iFaz  
- **License:** Apache 2.0 (permitting commercial and research use)  
- **Tags:**  
  - Text generation inference  
  - Transformers  
  - Unsloth  
  - LLaMA  
  - TRL (Transformers Reinforcement Learning)  

## Usage  
This model is designed for use in text-generation pipelines and can be easily integrated with the Hugging Face Transformers library. Its optimized architecture allows for inference on low-resource hardware, making it an excellent choice for applications that require efficient and scalable NLP solutions.

### Example Code:  
```python
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("unsloth/Llama-3.2-3B-Instruct-bnb-4bit")
model = AutoModelForCausalLM.from_pretrained("unsloth/Llama-3.2-3B-Instruct-bnb-4bit")

# Generate text
input_text = "Explain the benefits of AI in education."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Performance  
The fine-tuned model demonstrates strong performance on instruction-based tasks, providing detailed and contextually accurate responses. The 4-bit quantization enhances its speed and reduces memory consumption, enabling usage on devices with limited computational resources.

## Applications  
- **Conversational AI:** Develop chatbots and virtual assistants with coherent, context-aware dialogue generation.  
- **Text Summarization:** Extract concise summaries from lengthy texts for improved readability.  
- **Creative Writing:** Assist in generating stories, articles, or creative content.  
- **Education:** Enhance e-learning platforms with interactive and adaptive learning tools.  

## Limitations and Considerations  
- **Language Limitation:** Currently optimized for English. Performance on other languages may be suboptimal.  
- **Domain-Specific Knowledge:** While the model performs well on general tasks, it may require additional fine-tuning for domain-specific applications.  

## About the Developer  
This model was developed and fine-tuned by **iFaz**, leveraging the capabilities of the `unsloth/Llama-3.2-3B` architecture to create an efficient and high-performance NLP tool.  

## Acknowledgments  
The model builds upon the `unsloth/Llama-3.2-3B` framework and incorporates advancements in quantization techniques. Special thanks to the Hugging Face community for providing tools and resources to support NLP development.  

## License  
The model is distributed under the Apache 2.0 License, allowing for both research and commercial use. For more details, refer to the [license documentation](https://opensource.org/licenses/Apache-2.0).