💻 Echo-CodeEX (0.5B Parameters - GGUF)

Echo-CodeEX is a specialized, edge-optimized 0.5B parameter variant engineered explicitly for offline programming assistance, code execution logic, and structured syntax manipulation. Built upon a fine-tuned Qwen-2.5-Instruct architecture and fully merged into a standalone GGUF binary, it balances lightning-fast syntax completion with low-resource hardware execution.

✨ Key Features

• Syntax Grounded: Fine-tuned specifically to prioritize code construction, structural scripting loops, and algorithmic optimizations over open-ended narrative generation.
• Unified GGUF Engine: Zero dependencies on external floating adapter weights or complex Python multi-layer environments. Loadable instantly across standard local runtimes (llama.cpp, node-llama-cpp, Ollama).
• Fill-in-the-Middle (FIM) Ready: Inherits raw structural token patterns from the Qwen architecture, enabling seamless inline logic insertions and multi-line code predictions.

---

🧠 Code Prompt Engineering Structure

To bypass open-ended conversational filler and force direct code output, structure your inputs strictly within the ChatML layout. Define the system parameters explicitly to receive clean code blocks:

<|im_start|>system
You are Echo-CodeEX, an expert code generation assistant. Respond only with structured code blocks and clean syntax commentaries.<|im_end|>
<|im_start|>user
Write a clean Python function to parse JSON strings safely.<|im_end|>
<|im_start|>assistant

💻 Sample Implementation (Node.js)

You can spin this specialized model up locally inside your developer environment using node-llama-cpp:

import {LlamaModel, LlamaContext, LlamaSequence} from "node-llama-cpp";
import path from "path";

const model = new LlamaModel({
    modelPath: path.join(__dirname, "echo-codeex.gguf")
});

const context = new LlamaContext({model});
const sequence = new LlamaSequence({context});

const prompt = `<|im_start|>system\nYou are Echo-CodeEX.<|im_end|>\n<|im_start|>user\nWrite a basic bash script to check if a file exists.\n<|im_end|>\n<|im_start|>assistant\n`;
const tokens = model.tokenize(prompt);

console.log("Generating script output...");
const response = await sequence.evaluate(tokens, {
    temperature: 0.1 // Kept low to enforce syntax consistency over creativity
});
print(model.detokenize(response));

📄 License

This model's merged weights are distributed under the Apache 2.0 License, fully compliant with the core permissions and commercial deployment conditions set by the original Qwen development team.