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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use backpack-run/Qwen3-Coder-30B-A3B-Instruct-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf backpack-run/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf backpack-run/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 serve -hf backpack-run/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf backpack-run/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 backpack-run/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf backpack-run/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 backpack-run/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf backpack-run/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q4_K_M

Use Docker

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

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

How to use backpack-run/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 serve -hf backpack-run/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "backpack-run/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull backpack-run/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

Hermes Agent

How to use backpack-run/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 serve -hf backpack-run/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 backpack-run/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use backpack-run/Qwen3-Coder-30B-A3B-Instruct-GGUF with OpenClaw:

Start the llama.cpp server

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

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "backpack-run/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Qwen3-Coder-30B-A3B-Instruct — Backpack GGUF

🎒 Backpack Verified

GGUF quantizations of Qwen/Qwen3-Coder-30B-A3B-Instruct, tested for llama.cpp-compatible text inference and packaged for Backpack.

Model

Property	Value
Original model	`Qwen/Qwen3-Coder-30B-A3B-Instruct`
Original publisher	Qwen
Upstream revision	`b2cff646eb4bb1d68355c01b18ae02e7cf42d120`
Architecture	Qwen3MoeForCausalLM
Parameters	30,532,122,624
Context length	262,144
Input modalities	text
Output modalities	text
License	apache-2.0

Available packages

Quantization	Size	Approx. RAM	Recommended for
Q4_K_M	17.3 GiB	26.05 GB	Most users
Q5_K_M	20.2 GiB	30.33 GB	Higher quality
Q8_0	30.3 GiB	44.85 GB	Plenty of memory

Memory values are estimates, not guarantees. Runtime configuration and context length change actual use.

Backpack recommendation

Recommended: Q4_K_M. It usually offers a practical quality, size, and speed balance for local inference.

Run with llama.cpp

Using the llama.cpp revision recorded below:

llama-completion --model Qwen3-Coder-30B-A3B-Instruct-Q4_K_M.gguf -cnv

Run with Backpack

These artifacts and backpack-model.yaml are prepared for the Backpack AI workspace.

Validation

Package	Integrity	Load	Inference	Tokenizer
Q4_K_M	passed	passed	passed	passed
Q5_K_M	passed	passed	passed	passed
Q8_0	passed	passed	passed	passed

Capability qualification: code

Tested with Qwen3-Coder-30B-A3B-Instruct-Q4_K_M.gguf. These are deterministic smoke tests, not benchmark scores.

Capability	Status
chat	passed
code_generation	passed
structured_tool_arguments	passed
tool_calling	passed
multi_turn_tool_loop	passed

Packaged: 2026-08-30T21:24:08.172221+00:00
llama.cpp revision: bdf3955159d7184f44b76091973eeff532890a35
SHA-256 checksums: see checksums.sha256
Qwen3-Coder-30B-A3B-Instruct-Q4_K_M.gguf: 5f27bd8085b01b078454eb13e17d0c07cdd2d346f7033713a55d6c2c05c84c43
Qwen3-Coder-30B-A3B-Instruct-Q5_K_M.gguf: 7249b80c81e43dbbaeb7a68480577b0cf7360b751dd2392ad15039f0a46ee4b9
Qwen3-Coder-30B-A3B-Instruct-Q8_0.gguf: 8ec8ddf444b5ec40615ab0cb7fda0bf94483b3c5e6de1237991deca430d01b96

Provenance

The source model was resolved to immutable revision b2cff646eb4bb1d68355c01b18ae02e7cf42d120. It was converted with llama.cpp's convert_hf_to_gguf.py and quantized with llama-quantize; the exact tested revision is recorded above and in backpack-model.yaml.

License and attribution

Upstream declares apache-2.0. Review the upstream model card and comply with all applicable terms.

Backpack does not claim ownership of the original model. These artifacts are packaged and quantized distributions of the upstream model.

Disclaimer

Quantization can alter output quality. Memory estimates vary with runtime configuration, context length, and hardware.

Downloads last month: -

GGUF

Model size

31B params

Architecture

qwen3moe

Hardware compatibility

4-bit

5-bit

8-bit

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