README.md

---
license: apache-2.0
tags:
  - causal-lm
  - code-generation
  - edge-device
  - quantized
  - onnx
  - gguf
  - mobile
language:
  - en
library_name: transformers
pipeline_tag: text-generation
model-index:
  - name: CodeLlama-Edge-1.5B
    results: []
---
![license](https://img.shields.io/badge/license-Apache%202.0-blue.svg)
![model size](https://img.shields.io/badge/parameters-1.5B-green)
![quantized](https://img.shields.io/badge/format-GGUF%2FONNX%2FHF-yellow)
![optimized](https://img.shields.io/badge/optimized-for%20Edge%20Devices-orange)
[![Hugging Face Model](https://img.shields.io/badge/view_on-huggingface.co-blue?logo=huggingface)](https://huggingface.co/tommytracx/CodeLlama-Edge-1.5B)
# CodeLlama-Edge-1.5B

`CodeLlama-Edge-1.5B` is an edge-optimized variant of the CodeLlama series, designed to run efficiently on mobile and embedded devices using quantized or distilled formats.

## Model Description

- **Model Type**: Causal Language Model  
- **Base Model**: CodeLlama  
- **Optimizations**: Quantization-aware training, pruning, and edge-device compatibility  
- **Parameters**: 1.5 Billion  
- **Intended Use**: On-device coding assistance, embedded systems, low-power environments  

## Features

- Token-efficient for code generation  
- Ideal for IDEs, mobile apps, IoT dev tools  
- Low memory and compute footprint  

## Example Usage

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tommytracx/CodeLlama-Edge-1.5B")
model = AutoModelForCausalLM.from_pretrained("tommytracx/CodeLlama-Edge-1.5B")

input_text = "def quicksort(arr):"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=64)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## License

Apache 2.0

## Author

- Maintained by: [tommytracx](https://huggingface.co/tommytracx)