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

Custom chat template

by oPnf4fMoKMz4VFq - opened Feb 20

Discussion

oPnf4fMoKMz4VFq

Feb 20

In your blog post it's recommended to use the custom template which I assume is the template file in the repo?

I tried it with llama-server --chat-template-file template, but it fails to parse it. I'm probably just misunderstanding something about the template part.

common_chat_templates_init: error: lexer: unexpected character: $
...{- end }}<|im_end|>↵{{ end }}↵{{- range $i, $_ := .Messages }}↵{{- $last := eq (...
                                           ^
common_chat_templates_init: failed to initialize chat template
common_chat_templates_init: please consider disabling jinja via --no-jinja, or using another chat template

Ali93H

ByteShape org Feb 20

Thank you for reading the post and for paying close attention to the details ;)
The custom template is already baked into the GGUF file. If you run the GGUF with llama.cpp, that template is used by default.
The template file in the repo is for Ollama. Ollama uses Go templates rather than Jinja (what llama.cpp likes), so that file cannot be loaded directly with llama.cpp.

oPnf4fMoKMz4VFq

Feb 20

Thanks a lot for clarifying, didn't realize it's specific to Ollama. They could've standardized on Jinja.. and spare people from unnecessary extra work and confusion. Oh well :)

oPnf4fMoKMz4VFq changed discussion status to closed Feb 20

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