Instructions to use John1604/Qwen3-Coder-30B-A3B-Instruct-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use John1604/Qwen3-Coder-30B-A3B-Instruct-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="John1604/Qwen3-Coder-30B-A3B-Instruct-gguf",
	filename="Qwen3-Coder-30B-A3B-Instruct-f16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use John1604/Qwen3-Coder-30B-A3B-Instruct-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf John1604/Qwen3-Coder-30B-A3B-Instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf John1604/Qwen3-Coder-30B-A3B-Instruct-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf John1604/Qwen3-Coder-30B-A3B-Instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf John1604/Qwen3-Coder-30B-A3B-Instruct-gguf:Q4_K_M

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 John1604/Qwen3-Coder-30B-A3B-Instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf John1604/Qwen3-Coder-30B-A3B-Instruct-gguf:Q4_K_M

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 John1604/Qwen3-Coder-30B-A3B-Instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf John1604/Qwen3-Coder-30B-A3B-Instruct-gguf:Q4_K_M

Use Docker

docker model run hf.co/John1604/Qwen3-Coder-30B-A3B-Instruct-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use John1604/Qwen3-Coder-30B-A3B-Instruct-gguf with Ollama:
```
ollama run hf.co/John1604/Qwen3-Coder-30B-A3B-Instruct-gguf:Q4_K_M
```

Unsloth Studio new

How to use John1604/Qwen3-Coder-30B-A3B-Instruct-gguf 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 John1604/Qwen3-Coder-30B-A3B-Instruct-gguf 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 John1604/Qwen3-Coder-30B-A3B-Instruct-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for John1604/Qwen3-Coder-30B-A3B-Instruct-gguf to start chatting

Pi new

How to use John1604/Qwen3-Coder-30B-A3B-Instruct-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf John1604/Qwen3-Coder-30B-A3B-Instruct-gguf:Q4_K_M

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": "John1604/Qwen3-Coder-30B-A3B-Instruct-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use John1604/Qwen3-Coder-30B-A3B-Instruct-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf John1604/Qwen3-Coder-30B-A3B-Instruct-gguf:Q4_K_M

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 John1604/Qwen3-Coder-30B-A3B-Instruct-gguf:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use John1604/Qwen3-Coder-30B-A3B-Instruct-gguf with Docker Model Runner:
```
docker model run hf.co/John1604/Qwen3-Coder-30B-A3B-Instruct-gguf:Q4_K_M
```

Lemonade

How to use John1604/Qwen3-Coder-30B-A3B-Instruct-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull John1604/Qwen3-Coder-30B-A3B-Instruct-gguf:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-Coder-30B-A3B-Instruct-gguf-Q4_K_M

List all available models

lemonade list

Qwen3 Coder 30B A3B Instruct gguf

Make sure you have enough ram/gpu to run. On the right of model card, you may see the size of each quantized models.

Work with openclaw

It is recommended to be used with openclaw to have a personal secretary. please set context length as 64K to 256K to be used locally. Local api provided by this model will not expose your secret to the internet.

Use the model in ollama

First download and install ollama.

https://ollama.com/download

Command

in windows command line, or in terminal in ubuntu, type:

ollama run hf.co/John1604/Qwen3-Coder-30B-A3B-Instruct-gguf:q5_k_m

(q5_k_m is the model quant type, q5_k_s, q4_k_m, ..., can also be used)

C:\Users\developer>ollama run hf.co/John1604/Qwen3-Coder-30B-A3B-Instruct-gguf:q5_k_m

Use the model in LM Studio

download and install LM Studio

https://lmstudio.ai/

Discover models

In the LM Studio, click "Discover" icon. "Mission Control" popup window will be displayed.

In the "Mission Control" search bar, type "John1604/Qwen3-Coder-30B-A3B-Instruct-gguf" and check "GGUF", the model should be found.

Download the model.

You may choose quantized model.

Load the model.

Ask questions.

quantized models

Type	Bits	Quality	Description
Q2_K	2-bit	🟥 Low	Minimal footprint; only for tests
Q3_K_S	3-bit	🟧 Low	“Small” variant (less accurate)
Q3_K_M	3-bit	🟧 Low–Med	“Medium” variant
Q4_K_S	4-bit	🟨 Med	Small, faster, slightly less quality
Q4_K_M	4-bit	🟩 Med–High	“Medium” — best 4-bit balance
Q5_K_S	5-bit	🟩 High	Slightly smaller than Q5_K_M
Q5_K_M	5-bit	🟩🟩 High	Excellent general-purpose quant
Q6_K	6-bit	🟩🟩🟩 Very High	Almost FP16 quality, larger size
Q8_0	8-bit	🟩🟩🟩🟩	Near-lossless baseline

Downloads last month: 172

GGUF

Model size

31B params

Architecture

qwen3moe

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for John1604/Qwen3-Coder-30B-A3B-Instruct-gguf

Base model

Qwen/Qwen3-Coder-30B-A3B-Instruct

Quantized

(140)

this model