CoolFace
Modelpublic

giladgd/Qwen3-Coder-30B-A3B-Instruct-Q4_K_M-GGUF

sourceHugging Faceapache-2.0updated 1y agoView on Hugging Face
5likes2.1kdownloads
Model Card

giladgd/Qwen3-Coder-30B-A3B-Instruct-Q4KM-GGUF

This model was converted to GGUF format from `Qwen/Qwen3-Coder-30B-A3B-Instruct` using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Use it with node-llama-cpp (recommended)

CLI

Chat with the model using this command:

bash
npx -y node-llama-cpp chat hf:giladgd/Qwen3-Coder-30B-A3B-Instruct-Q4_K_M-GGUF:Q4_K_M
Ensure that you have node.js installed first: ``bash brew install nodejs ``

Code

Use it in your node.js project:

bash
npm install node-llama-cpp
typescript
import {getLlama, resolveModelFile, LlamaChatSession} from "node-llama-cpp";

const modelUri = "hf:giladgd/Qwen3-Coder-30B-A3B-Instruct-Q4_K_M-GGUF:Q4_K_M";


const llama = await getLlama();
const model = await llama.loadModel({
    modelPath: await resolveModelFile(modelUri)
});
const context = await model.createContext();
const session = new LlamaChatSession({
    contextSequence: context.getSequence()
});


const q1 = "Hi there, how are you?";
console.log("User: " + q1);

const a1 = await session.prompt(q1);
console.log("AI: " + a1);

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

bash
brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

bash
llama-cli --hf-repo giladgd/Qwen3-Coder-30B-A3B-Instruct-Q4_K_M-GGUF --hf-file qwen3-coder-30b-a3b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

bash
llama-server --hf-repo giladgd/Qwen3-Coder-30B-A3B-Instruct-Q4_K_M-GGUF --hf-file qwen3-coder-30b-a3b-instruct-q4_k_m.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo giladgd/Qwen3-Coder-30B-A3B-Instruct-Q4_K_M-GGUF --hf-file qwen3-coder-30b-a3b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"

or 

./llama-server --hf-repo giladgd/Qwen3-Coder-30B-A3B-Instruct-Q4_K_M-GGUF --hf-file qwen3-coder-30b-a3b-instruct-q4_k_m.gguf -c 2048