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 Settings

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

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

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

One-Shot Hero! (python, game, simulation, transform intent to code)

by BingoBird - opened Mar 26

Discussion

BingoBird

Mar 26

•

edited Mar 26

Hi! Non-Agentic test here performed via batch on 86 small (<13GB models)

THIS ONE IS THE WINNER!
At 2.6bpw it beat all Qwen3.5 all GPT-OSS All GLM-Flash with thinking off.

here's the prompt it solved:

- All balls have the same radius.
- All balls drop from the heptagon center when starting.
- Colors are: #f8b862, #f6ad49, #f39800, #f08300, #ec6d51, #ee7948, #ed6d3d, #ec6800, #ec6800, #ee7800, #eb6238, #ea5506, #ea5506, #eb6101, #e49e61, #e45e32, #e17b34, #dd7a56, #db8449, #d66a35
- The balls should be affected by gravity and friction, and they must be contained within the area of the heptagon by physical collision detection, making the balls bounce off the rotating walls realistically. There should also be collisions between balls.
- The heptagon is spinning around its center, rotating a full cycle once every 5 seconds.
- The heptagon size should be large enough to contain all the balls.
- Do not use the pygame library; implement collision detection algorithms and collision response etc. by yourself. The following python libraries are allowed: tkinter, math, numpy, dataclasses, typing, sys.
- All program code should be put in a single python file, with shebang for execution from bash shell.

Unfortunately i had a dataloss on the drive but here are the next-best models:

amoral-cogito-14b-Q4_K_M.gguf-test2.py 1 ball, xor lines
amoral-cogito-14b-Q4_K_M.gguf-test.py 0 ball, xorlines
Dobby-Mini-Unhinged-Llama-3.1-8B.i1-Q4_K_M.gguf-test.py one ball no collisions
gemma-3-12b-it-antislop.i1-IQ4_XS.gguf-test2.py  no clearing of balls
gemma-3-12b-it-norm-preserved-biprojected-abliterated.i1-IQ4_XS.gguf-test2.py  Balls outside
gemma-3-amoral-12B-v2.i1-IQ4_NL.gguf-test2.py no heptagon crazy ball physics
gemma-3-amoral-12B-v2.i1-IQ4_NL.gguf-test.py crazy motion/collision
GLM-4.6V-Flash-Q4_K_M.gguf-test2.py no visible heptagon, good ball motion
Mamba-Codestral-7B-v0.1-Q5_0.gguf-test2.py blank screen
Ministral-3-8B-Reasoning-2512-Q5_K_M.gguf-test2.py no balls
Mistral-Small-3.2-24B-Instruct-2506-IQ4_XS-bartowski.gguf-test.py
Mistral-Small-3.2-24B-Instruct-2506.Q4_K_H.gguf-test.py bad collisions
NousResearch_Hermes-4-14B-IQ4_NL.gguf-test2.py only one ball, too slow gravity, too fast spinning
NousResearch_Hermes-4-14B-IQ4_NL.gguf-test.py  one ball, too fast heptagon, too slow ball
Qwen3-4B-Instruct-2507-Q4_K_M.gguf-test2.py  no visible heptagion, too slow ball motion, no collision
Qwen3-4B-Instruct-2507-sombliterated-Q8_0.gguf-test.py no balls, good heptagon motion
Qwen3-Coder-30B-A3B-Instruct-Pruned-Q3_K_M.gguf-test2.py  bad collisions, slow motion

Qwen3-Coder-30B-A3B-Instruct-Q3_K_S-2.69bpw.gguf-test.py BEST PERFECT

This test is from a 1-yr old reddit thread where SOTA online models were tested and fared no better than this, which was perfect except rotation speed was set too high.

Anyway, wow. wow..

BingoBird

Mar 27

https://x0.at/UbDs.py

Only changed the rotation speed....

Wonder how much it costs in GPU time to do byteshape tuned quants?

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