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

Libraries

How to use byteshape/Qwen3-Coder-30B-A3B-Instruct-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="byteshape/Qwen3-Coder-30B-A3B-Instruct-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("byteshape/Qwen3-Coder-30B-A3B-Instruct-GGUF", dtype="auto")

llama-cpp-python

How to use byteshape/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="byteshape/Qwen3-Coder-30B-A3B-Instruct-GGUF",
	filename="Qwen3-Coder-30B-A3B-Instruct-IQ3_S-2.66bpw.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 byteshape/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 byteshape/Qwen3-Coder-30B-A3B-Instruct-GGUF:IQ3_S
# Run inference directly in the terminal:
llama-cli -hf byteshape/Qwen3-Coder-30B-A3B-Instruct-GGUF:IQ3_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf byteshape/Qwen3-Coder-30B-A3B-Instruct-GGUF:IQ3_S
# Run inference directly in the terminal:
llama-cli -hf byteshape/Qwen3-Coder-30B-A3B-Instruct-GGUF:IQ3_S

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 byteshape/Qwen3-Coder-30B-A3B-Instruct-GGUF:IQ3_S
# Run inference directly in the terminal:
./llama-cli -hf byteshape/Qwen3-Coder-30B-A3B-Instruct-GGUF:IQ3_S

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 byteshape/Qwen3-Coder-30B-A3B-Instruct-GGUF:IQ3_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf byteshape/Qwen3-Coder-30B-A3B-Instruct-GGUF:IQ3_S

Use Docker

docker model run hf.co/byteshape/Qwen3-Coder-30B-A3B-Instruct-GGUF:IQ3_S

LM Studio
Jan

vLLM

How to use byteshape/Qwen3-Coder-30B-A3B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "byteshape/Qwen3-Coder-30B-A3B-Instruct-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "byteshape/Qwen3-Coder-30B-A3B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/byteshape/Qwen3-Coder-30B-A3B-Instruct-GGUF:IQ3_S

SGLang

How to use byteshape/Qwen3-Coder-30B-A3B-Instruct-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "byteshape/Qwen3-Coder-30B-A3B-Instruct-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "byteshape/Qwen3-Coder-30B-A3B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "byteshape/Qwen3-Coder-30B-A3B-Instruct-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "byteshape/Qwen3-Coder-30B-A3B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use byteshape/Qwen3-Coder-30B-A3B-Instruct-GGUF with Ollama:
```
ollama run hf.co/byteshape/Qwen3-Coder-30B-A3B-Instruct-GGUF:IQ3_S
```

Unsloth Studio new

How to use byteshape/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 byteshape/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 byteshape/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 byteshape/Qwen3-Coder-30B-A3B-Instruct-GGUF to start chatting

Pi new

How to use byteshape/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 byteshape/Qwen3-Coder-30B-A3B-Instruct-GGUF:IQ3_S

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": "byteshape/Qwen3-Coder-30B-A3B-Instruct-GGUF:IQ3_S"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use byteshape/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 byteshape/Qwen3-Coder-30B-A3B-Instruct-GGUF:IQ3_S

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 byteshape/Qwen3-Coder-30B-A3B-Instruct-GGUF:IQ3_S

Run Hermes

hermes

Docker Model Runner
How to use byteshape/Qwen3-Coder-30B-A3B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/byteshape/Qwen3-Coder-30B-A3B-Instruct-GGUF:IQ3_S
```

Lemonade

How to use byteshape/Qwen3-Coder-30B-A3B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull byteshape/Qwen3-Coder-30B-A3B-Instruct-GGUF:IQ3_S

Run and chat with the model

lemonade run user.Qwen3-Coder-30B-A3B-Instruct-GGUF-IQ3_S

List all available models

lemonade list

Qwen3-Coder-30B-A3B-Instruct GGUF (ShapeLearn Quantized)

This is a GGUF-quantized version of Qwen3-Coder-30B-A3B-Instruct produced with ByteShape's ShapeLearn, which learns the optimal datatype per tensor to maintain high quality even at very low bitlengths.

To learn more about ShapeLearn and to see detailed benchmarks across GPUs, CPUs, and even the Raspberry Pi, please visit our blog.

If you have questions or want to share feedback, reach us on Reddit.

How to Pick a Model

We provide CPU and GPU optimized variants for llama.cpp:

CPUs: Models labeled as KQ, optimized for CPU inference with predominantly KQ quantization.
GPUs: Models labeled as IQ, optimized for GPU inference with a hybrid approach combining KQ and IQ quantization for better throughput.

Each hardware target includes a range of models covering different size and quality tradeoffs.

The chart below shows quality versus tokens per second (TPS), with Unsloth used as the baseline for comparison. Quality is measured across five benchmarks, including function calling: BFCL-V3, LiveCodeBench V6, HumanEval, Math500, and GSM8K.

Selection rule: Choose the model with the highest quality at your target throughput or the fastest model that still meets your required quality.

CPU Models

Table sorted by model size (match the chart numbers to model IDs):

Model ID	Bits/Weight	Model Size
KQ-1	2.65	10.1 GB
KQ-2	2.66	10.2 GB
KQ-3	2.69	10.3 GB
KQ-4	2.69	10.3 GB
KQ-5	2.90	11.1 GB
KQ-6	3.00	11.5 GB
KQ-7	3.31	12.7 GB
KQ-8	4.20	16.0 GB

GPU Models

Table sorted by model size (match the chart numbers to model IDs):

Model ID	Bits/Weight	Model Size
IQ-1	2.66	10.1 GB
IQ-2	2.68	10.2 GB
IQ-3	2.83	10.8 GB
IQ-4	3.12	11.9 GB
IQ-5	3.48	13.3 GB
IQ-6	4.20	16.0 GB

Notes on quantization labels

The labels you see (for example IQ4_XS) are only there to make Hugging Face show our models in the GGUF table. We do not use the conventional quantization profiles as defined in llama.cpp. In our case, these labels indicate the primary quantization approach and average bit length. Note that both KQ and IQ models may use a mix of quantization techniques optimized for their target hardware, which is why several models can share the same tag.

Running these models with Ollama

All GGUF files in this repo can be used directly with Ollama.

To run a model with Ollama, use:

ollama run hf.co/byteshape/Qwen3-Coder-30B-A3B-Instruct-GGUF:FILE_NAME.gguf

Replace FILE_NAME.gguf with the GGUF filename you want. For example:

ollama run hf.co/byteshape/Qwen3-Coder-30B-A3B-Instruct-GGUF:Qwen3-Coder-30B-A3B-Instruct-IQ4_XS-4.20bpw.gguf

Downloads last month: 2,875

GGUF

Model size

31B params

Architecture

qwen3moe

Hardware compatibility

3-bit

4-bit

Model tree for byteshape/Qwen3-Coder-30B-A3B-Instruct-GGUF

Base model

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

Quantized

(143)

this model