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

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