Instructions to use Jershone/Echo-CodeEX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Jershone/Echo-CodeEX with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -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 serve -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
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
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 serve -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
- OpenClaw new
How to use Jershone/Echo-CodeEX with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Jershone/Echo-CodeEX
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "Jershone/Echo-CodeEX" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- 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
- Hermes Agent
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 serve -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
- Atomic Chat
| 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: | |
| ```text | |
| <|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: | |
| ```JavaScript | |
| 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. |