Instructions to use backpack-run/Qwen3-Coder-Next-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-Next-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-Next-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf backpack-run/Qwen3-Coder-Next-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-Next-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf backpack-run/Qwen3-Coder-Next-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-Next-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf backpack-run/Qwen3-Coder-Next-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-Next-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf backpack-run/Qwen3-Coder-Next-GGUF:Q4_K_M

Use Docker

docker model run hf.co/backpack-run/Qwen3-Coder-Next-GGUF:Q4_K_M

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

How to use backpack-run/Qwen3-Coder-Next-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-Next-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-Next-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

How to use backpack-run/Qwen3-Coder-Next-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull backpack-run/Qwen3-Coder-Next-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-Coder-Next-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use backpack-run/Qwen3-Coder-Next-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-Next-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-Next-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use backpack-run/Qwen3-Coder-Next-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-Next-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-Next-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-Next-GGUF — Backpack GGUF

🎒 Backpack Verified

GGUF quantizations of Qwen/Qwen3-Coder-Next-GGUF, tested for llama.cpp-compatible text inference and packaged for Backpack. The original publisher produced these GGUF files; Backpack verified and repackaged the immutable artifacts without re-quantizing them.

Model

Property	Value
Original model	`Qwen/Qwen3-Coder-Next-GGUF`
Original publisher	Qwen
Upstream revision	`b82fb7382639d97b38fa7672e526c760c2fb358e`
Architecture	Qwen3NextForCausalLM
Parameters	79,674,391,296
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	45.1 GiB	66.35 GB	Most users
Q5_K_M	52.8 GiB	77.56 GB	Higher quality
Q8_0	79.0 GiB	115.5 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-Next-Q4_K_M/Qwen3-Coder-Next-Q4_K_M-00001-of-00004.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-Next-Q4_K_M-00001-of-00004.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: 2026-08-31T11:20:27.760656+00:00
llama.cpp revision: bdf3955159d7184f44b76091973eeff532890a35
SHA-256 checksums: see checksums.sha256
Qwen3-Coder-Next-Q4_K_M/Qwen3-Coder-Next-Q4_K_M-00001-of-00004.gguf: 6bcfc9f9c37901eeb92172e2ab871224dab36a453d263bcb2547f737409534da
Qwen3-Coder-Next-Q4_K_M/Qwen3-Coder-Next-Q4_K_M-00002-of-00004.gguf: 817def0691ee9d08bf3dc4444be7aed29c9e52091e8fa9d97901ce7e7f6f01d3
Qwen3-Coder-Next-Q4_K_M/Qwen3-Coder-Next-Q4_K_M-00003-of-00004.gguf: 23aa634d47dca9b4ca3ea249384e6f01951b24c83cdc076f37f6f43d6c99883f
Qwen3-Coder-Next-Q4_K_M/Qwen3-Coder-Next-Q4_K_M-00004-of-00004.gguf: 249c768cc5f130dc731567d6edcbdacc48e14dec9e02c5dbe2b2185d2c5bdb2b
Qwen3-Coder-Next-Q5_K_M/Qwen3-Coder-Next-Q5_K_M-00001-of-00004.gguf: d68162877891c4ba309c8601a69721ecfc7be2091ad99e8175a078ae59decdd3
Qwen3-Coder-Next-Q5_K_M/Qwen3-Coder-Next-Q5_K_M-00002-of-00004.gguf: 3da1a3fda1061009126eb48c7098221653ab63e0a266d3b6d04f5eac3ae9aa74
Qwen3-Coder-Next-Q5_K_M/Qwen3-Coder-Next-Q5_K_M-00003-of-00004.gguf: df60c7a55c6e118d4984a829b74df6b9e863dcea749ee7dc25ab5e72913624e1
Qwen3-Coder-Next-Q5_K_M/Qwen3-Coder-Next-Q5_K_M-00004-of-00004.gguf: 777ac63a6f94272934996b1cfd7e5619c6804e8580ec4e00311d2a465e298c6a
Qwen3-Coder-Next-Q8_0/Qwen3-Coder-Next-Q8_0-00001-of-00004.gguf: 30b7554fc0c846a5dc3ecf585884c77471f73e3da698a8ba4fabd8e7868c6533
Qwen3-Coder-Next-Q8_0/Qwen3-Coder-Next-Q8_0-00002-of-00004.gguf: 3f96379de5a5c4655cb378710ea571d5e9cc96f260120a44a6477198efcdc27d
Qwen3-Coder-Next-Q8_0/Qwen3-Coder-Next-Q8_0-00003-of-00004.gguf: 5dd1ce07eaae95ee430331dc9c6f3120ff88e4211ad3a0cceeaa963f25328504
Qwen3-Coder-Next-Q8_0/Qwen3-Coder-Next-Q8_0-00004-of-00004.gguf: 76730702c630bf76305139165cb85421858604030851dfd64fe96a5e67cda99d

Provenance

The source model was resolved to immutable revision b82fb7382639d97b38fa7672e526c760c2fb358e. The GGUF files were quantized by Qwen and verified by Backpack against the pinned llama.cpp revision recorded above.

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. Backpack did not quantize the upstream GGUF artifacts.

Disclaimer

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

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GGUF

Model size

80B params

Architecture

qwen3next

Hardware compatibility

4-bit

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