Instructions to use Jershone/Echo-CodeEX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jershone/Echo-CodeEX with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Jershone/Echo-CodeEX",
	filename="EchoAI-CodeEX.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Jershone/Echo-CodeEX with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Jershone/Echo-CodeEX
# Run inference directly in the terminal:
llama-cli -hf Jershone/Echo-CodeEX

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Jershone/Echo-CodeEX
# Run inference directly in the terminal:
llama-cli -hf Jershone/Echo-CodeEX

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Jershone/Echo-CodeEX
# Run inference directly in the terminal:
./llama-cli -hf Jershone/Echo-CodeEX

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Jershone/Echo-CodeEX
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Jershone/Echo-CodeEX

Use Docker

docker model run hf.co/Jershone/Echo-CodeEX

LM Studio
Jan

vLLM

How to use Jershone/Echo-CodeEX with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jershone/Echo-CodeEX"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jershone/Echo-CodeEX",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Jershone/Echo-CodeEX

Ollama
How to use Jershone/Echo-CodeEX with Ollama:
```
ollama run hf.co/Jershone/Echo-CodeEX
```

Unsloth Studio new

How to use Jershone/Echo-CodeEX with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Jershone/Echo-CodeEX to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Jershone/Echo-CodeEX to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Jershone/Echo-CodeEX to start chatting

Pi new

How to use Jershone/Echo-CodeEX with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Jershone/Echo-CodeEX

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Jershone/Echo-CodeEX"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Jershone/Echo-CodeEX with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Jershone/Echo-CodeEX

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Jershone/Echo-CodeEX

Run Hermes

hermes

Docker Model Runner
How to use Jershone/Echo-CodeEX with Docker Model Runner:
```
docker model run hf.co/Jershone/Echo-CodeEX
```

Lemonade

How to use Jershone/Echo-CodeEX with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Jershone/Echo-CodeEX

Run and chat with the model

lemonade run user.Echo-CodeEX-{{QUANT_TAG}}

List all available models

lemonade list

Echo-CodeEX / README.md

Jerry

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metadata

license: apache-2.0
base_model: Qwen/Qwen2.5-0.5B-Instruct
tags:
  - gguf
  - code
  - text-generation
  - edge-ai
  - qwen
model_creator: MLM8372984732947
model_name: Echo-CodeEX-GGUF
pipeline_tag: text-generation
language:
  - en

💻 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.