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

Fantastic performance!

by sumarj - opened Feb 21

Discussion

sumarj

Feb 21

I use this model with Opencode and especially the prompt eval is crazy fast. Well done!

MaximusAI

Feb 22

Good job done by your team,

with other quants (q4_x) I get low 20ish tps on my hardware (3080 12GB) with yours I get a lot more

Thanks for the quants and your hard work.

enrique-torres

ByteShape org Feb 22

Thank you for the kind words of appreciation! This is the kind of feedback that motivates us! If you find any weird edge cases from the models, or cases where they don't perform well at all, let us know! We're trying to amplify our benchmarking as much as possible and we want to be able to capture those in the future

sumarj

Feb 22

•

edited Feb 22

I managed to run some benchmarks against other IQ4 and Q4 models. This is a system that is a bit CPU bound with an old Xeon-W with only 3.6Ghz and 2 x 5060 Ti 16GB connected at PCIe3.0 x8. My primary use case is local coding and I use opencode (which I really like). Therefore prompt eval is my main concern. tg is not that important but when the agent is reading lots of files or docs to explore a solution, waiting for prompt eval is really annoying.
Also in this light I set the benchmark to run prompt eval at 32768 tokens. I find these long contexts is where this model works really well.
I compiled llama.cpp for blackwell support with CUDA arch 120a.

llama-cpp-server  | load_backend: loaded BLAS backend from /app/libggml-blas.so
llama-cpp-server  | ggml_cuda_init: found 2 CUDA devices:
llama-cpp-server  |   Device 0: NVIDIA GeForce RTX 5060 Ti, compute capability 12.0, VMM: yes
llama-cpp-server  |   Device 1: NVIDIA GeForce RTX 5060 Ti, compute capability 12.0, VMM: yes
llama-cpp-server  | load_backend: loaded CUDA backend from /app/libggml-cuda.so
llama-cpp-server  | load_backend: loaded CPU backend from /app/libggml-cpu-skylakex.so
llama-cpp-server  | main: n_parallel is set to auto, using n_parallel = 4 and kv_unified = true
llama-cpp-server  | build: 8125 (e877ad8bd) with GNU 13.3.0 for Linux x86_64
llama-cpp-server  | system info: n_threads = 4, n_threads_batch = 4, total_threads = 4
llama-cpp-server  |
llama-cpp-server  | system_info: n_threads = 4 (n_threads_batch = 4) / 4 | CUDA : ARCHS = 1200 | USE_GRAPHS = 1 | PEER_MAX_BATCH_SIZE = 128 | BLACKWELL_NATIVE_FP4 = 1 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | AVX512 = 1 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 |
llama-cpp-server  |
llama-cpp-server  | | model                                        |       size |     params | backend    | threads |            test |                  t/s | Full Model                                                 |
llama-cpp-server  | | -------------------------------------------- | ---------: | ---------: | ---------- | ------: | --------------: | -------------------: | ---------------------------------------------------------: |
llama-cpp-server  | | deepseek2 30B.A3B IQ4_XS - 4.25 bpw          |  15.15 GiB |    29.94 B | BLAS,CUDA  |       4 |           pp512 |       2553.33 ± 4.81 | unsloth/GLM-4.7-Flash-IQ4_XS.gguf                          |
llama-cpp-server  | | deepseek2 30B.A3B IQ4_XS - 4.25 bpw          |  15.15 GiB |    29.94 B | BLAS,CUDA  |       4 |          pp1024 |       2728.10 ± 5.33 |                                                            |
llama-cpp-server  | | deepseek2 30B.A3B IQ4_XS - 4.25 bpw          |  15.15 GiB |    29.94 B | BLAS,CUDA  |       4 |         pp32768 |        740.10 ± 1.10 |                                                            |
llama-cpp-server  | | deepseek2 30B.A3B IQ4_XS - 4.25 bpw          |  15.15 GiB |    29.94 B | BLAS,CUDA  |       4 |           tg128 |         76.51 ± 0.73 |                                                            |
llama-cpp-server  | | deepseek2 30B.A3B IQ4_XS - 4.25 bpw          |  15.15 GiB |    29.94 B | BLAS,CUDA  |       4 |           tg512 |         71.20 ± 0.34 |                                                            |
llama-cpp-server  | | deepseek2 30B.A3B IQ4_XS - 4.25 bpw          |  15.15 GiB |    29.94 B | BLAS,CUDA  |       4 |          tg3072 |         53.34 ± 0.04 |                                                            |
llama-cpp-server  | | deepseek2 30B.A3B Q4_K - Medium              |  13.14 GiB |    23.00 B | BLAS,CUDA  |       4 |           pp512 |      2584.51 ± 10.42 | unsloth/GLM-4.7-Flash-REAP-23B-A3B-Q4_K_M.gguf             |
llama-cpp-server  | | deepseek2 30B.A3B Q4_K - Medium              |  13.14 GiB |    23.00 B | BLAS,CUDA  |       4 |          pp1024 |      2749.52 ± 16.24 |                                                            |
llama-cpp-server  | | deepseek2 30B.A3B Q4_K - Medium              |  13.14 GiB |    23.00 B | BLAS,CUDA  |       4 |         pp32768 |        722.07 ± 0.47 |                                                            |
llama-cpp-server  | | deepseek2 30B.A3B Q4_K - Medium              |  13.14 GiB |    23.00 B | BLAS,CUDA  |       4 |           tg128 |         71.73 ± 0.52 |                                                            |
llama-cpp-server  | | deepseek2 30B.A3B Q4_K - Medium              |  13.14 GiB |    23.00 B | BLAS,CUDA  |       4 |           tg512 |         66.90 ± 0.29 |                                                            |
llama-cpp-server  | | deepseek2 30B.A3B Q4_K - Medium              |  13.14 GiB |    23.00 B | BLAS,CUDA  |       4 |          tg3072 |         50.83 ± 0.07 |                                                            |
llama-cpp-server  | | nemotron_h_moe 31B.A3.5B IQ4_XS - 4.25 bpw   |  16.91 GiB |    31.58 B | BLAS,CUDA  |       4 |           pp512 |      2710.09 ± 17.99 | unsloth/Nemotron-3-Nano-30B-A3B-IQ4_XS.gguf                |
llama-cpp-server  | | nemotron_h_moe 31B.A3.5B IQ4_XS - 4.25 bpw   |  16.91 GiB |    31.58 B | BLAS,CUDA  |       4 |          pp1024 |      3451.64 ± 16.10 |                                                            |
llama-cpp-server  | | nemotron_h_moe 31B.A3.5B IQ4_XS - 4.25 bpw   |  16.91 GiB |    31.58 B | BLAS,CUDA  |       4 |         pp32768 |       2649.96 ± 5.02 |                                                            |
llama-cpp-server  | | nemotron_h_moe 31B.A3.5B IQ4_XS - 4.25 bpw   |  16.91 GiB |    31.58 B | BLAS,CUDA  |       4 |           tg128 |        113.09 ± 1.26 |                                                            |
llama-cpp-server  | | nemotron_h_moe 31B.A3.5B IQ4_XS - 4.25 bpw   |  16.91 GiB |    31.58 B | BLAS,CUDA  |       4 |           tg512 |        113.50 ± 0.28 |                                                            |
llama-cpp-server  | | nemotron_h_moe 31B.A3.5B IQ4_XS - 4.25 bpw   |  16.91 GiB |    31.58 B | BLAS,CUDA  |       4 |          tg3072 |        112.43 ± 0.15 |                                                            |
llama-cpp-server  | | qwen3moe 30B.A3B IQ4_XS - 4.25 bpw (guessed) |  14.91 GiB |    30.53 B | BLAS,CUDA  |       4 |           pp512 |      2561.64 ± 13.29 | Byteshape/Qwen3-Coder-30B-A3B-Instruct-IQ4_XS-4.20bpw.gguf |
llama-cpp-server  | | qwen3moe 30B.A3B IQ4_XS - 4.25 bpw (guessed) |  14.91 GiB |    30.53 B | BLAS,CUDA  |       4 |          pp1024 |       2830.24 ± 6.13 |                                                            |
llama-cpp-server  | | qwen3moe 30B.A3B IQ4_XS - 4.25 bpw (guessed) |  14.91 GiB |    30.53 B | BLAS,CUDA  |       4 |         pp32768 |        849.71 ± 0.35 | <----                                                      |
llama-cpp-server  | | qwen3moe 30B.A3B IQ4_XS - 4.25 bpw (guessed) |  14.91 GiB |    30.53 B | BLAS,CUDA  |       4 |           tg128 |        108.97 ± 1.89 |                                                            |
llama-cpp-server  | | qwen3moe 30B.A3B IQ4_XS - 4.25 bpw (guessed) |  14.91 GiB |    30.53 B | BLAS,CUDA  |       4 |           tg512 |        103.27 ± 0.62 |                                                            |
llama-cpp-server  | | qwen3moe 30B.A3B IQ4_XS - 4.25 bpw (guessed) |  14.91 GiB |    30.53 B | BLAS,CUDA  |       4 |          tg3072 |        100.00 ± 0.25 |                                                            |
llama-cpp-server  | | deepseek2 30B.A3B IQ4_XS - 4.25 bpw          |  11.70 GiB |    23.00 B | BLAS,CUDA  |       4 |           pp512 |      2776.45 ± 14.63 | unsloth/GLM-4.7-Flash-REAP-23B-A3B-IQ4_XS.gguf             |
llama-cpp-server  | | deepseek2 30B.A3B IQ4_XS - 4.25 bpw          |  11.70 GiB |    23.00 B | BLAS,CUDA  |       4 |          pp1024 |      2938.64 ± 11.51 |                                                            |
llama-cpp-server  | | deepseek2 30B.A3B IQ4_XS - 4.25 bpw          |  11.70 GiB |    23.00 B | BLAS,CUDA  |       4 |         pp32768 |        731.76 ± 0.33 |                                                            |
llama-cpp-server  | | deepseek2 30B.A3B IQ4_XS - 4.25 bpw          |  11.70 GiB |    23.00 B | BLAS,CUDA  |       4 |           tg128 |         73.46 ± 0.03 |                                                            |
llama-cpp-server  | | deepseek2 30B.A3B IQ4_XS - 4.25 bpw          |  11.70 GiB |    23.00 B | BLAS,CUDA  |       4 |           tg512 |         68.25 ± 0.16 |                                                            |
llama-cpp-server  | | deepseek2 30B.A3B IQ4_XS - 4.25 bpw          |  11.70 GiB |    23.00 B | BLAS,CUDA  |       4 |          tg3072 |         51.71 ± 0.06 |                                                            |

I have marked the value that is most important to me. This model is noticeably quicker at pro cessing long prompts. Nemotron is quicker still but also a lot worse at coding.
My previous daily driver was unsloth/GLM-4.7-Flash-REAP-23B-A3B-Q4_K_M.gguf which is "only" about 120t/s slower at prompt eval but these numbers don't really do it justice. The Byteshape model feels a lot faster.

Ali93H

ByteShape org Feb 22

This is incredibly helpful, thank you for taking the time to run and share these benchmarks, especially at 32K context. Really appreciate the detailed setup and comparisons. Feedback like this genuinely helps us improve our work and future releases.

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