Qwen3-Coder-Next-REAP-48B-A3B-GGUF

Instructions to use Ai2-Alliance/Qwen3-Coder-Next-REAP-48B-A3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ai2-Alliance/Qwen3-Coder-Next-REAP-48B-A3B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Ai2-Alliance/Qwen3-Coder-Next-REAP-48B-A3B-GGUF",
	filename="BF16/Qwen3-Coder-Next-REAP-48B-A3B-BF16-00001-of-00006.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Ai2-Alliance/Qwen3-Coder-Next-REAP-48B-A3B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Ai2-Alliance/Qwen3-Coder-Next-REAP-48B-A3B-GGUF:Q4_K_XL
# Run inference directly in the terminal:
llama-cli -hf Ai2-Alliance/Qwen3-Coder-Next-REAP-48B-A3B-GGUF:Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Ai2-Alliance/Qwen3-Coder-Next-REAP-48B-A3B-GGUF:Q4_K_XL
# Run inference directly in the terminal:
llama-cli -hf Ai2-Alliance/Qwen3-Coder-Next-REAP-48B-A3B-GGUF:Q4_K_XL

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 Ai2-Alliance/Qwen3-Coder-Next-REAP-48B-A3B-GGUF:Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf Ai2-Alliance/Qwen3-Coder-Next-REAP-48B-A3B-GGUF:Q4_K_XL

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 Ai2-Alliance/Qwen3-Coder-Next-REAP-48B-A3B-GGUF:Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Ai2-Alliance/Qwen3-Coder-Next-REAP-48B-A3B-GGUF:Q4_K_XL

Use Docker

docker model run hf.co/Ai2-Alliance/Qwen3-Coder-Next-REAP-48B-A3B-GGUF:Q4_K_XL

LM Studio
Jan
Ollama
How to use Ai2-Alliance/Qwen3-Coder-Next-REAP-48B-A3B-GGUF with Ollama:
```
ollama run hf.co/Ai2-Alliance/Qwen3-Coder-Next-REAP-48B-A3B-GGUF:Q4_K_XL
```

Unsloth Studio

How to use Ai2-Alliance/Qwen3-Coder-Next-REAP-48B-A3B-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 Ai2-Alliance/Qwen3-Coder-Next-REAP-48B-A3B-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 Ai2-Alliance/Qwen3-Coder-Next-REAP-48B-A3B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Ai2-Alliance/Qwen3-Coder-Next-REAP-48B-A3B-GGUF to start chatting

How to use Ai2-Alliance/Qwen3-Coder-Next-REAP-48B-A3B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Ai2-Alliance/Qwen3-Coder-Next-REAP-48B-A3B-GGUF:Q4_K_XL

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": "Ai2-Alliance/Qwen3-Coder-Next-REAP-48B-A3B-GGUF:Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Ai2-Alliance/Qwen3-Coder-Next-REAP-48B-A3B-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 Ai2-Alliance/Qwen3-Coder-Next-REAP-48B-A3B-GGUF:Q4_K_XL

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 Ai2-Alliance/Qwen3-Coder-Next-REAP-48B-A3B-GGUF:Q4_K_XL

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use Ai2-Alliance/Qwen3-Coder-Next-REAP-48B-A3B-GGUF with Docker Model Runner:
```
docker model run hf.co/Ai2-Alliance/Qwen3-Coder-Next-REAP-48B-A3B-GGUF:Q4_K_XL
```

Lemonade

How to use Ai2-Alliance/Qwen3-Coder-Next-REAP-48B-A3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Ai2-Alliance/Qwen3-Coder-Next-REAP-48B-A3B-GGUF:Q4_K_XL

Run and chat with the model

lemonade run user.Qwen3-Coder-Next-REAP-48B-A3B-GGUF-Q4_K_XL

List all available models

lemonade list

Qwen3-Coder-Next-REAP-48B-A3B has the following specifications:

Type: Causal Language Models
Number of Parameters: 48B in total and 3B activated
Hidden Dimension: 2048
Number of Layers: 48
Hybrid Layout: 12 * (3 * (Gated DeltaNet -> MoE) -> 1 * (Gated Attention -> MoE))
Gated Attention:
Number of Attention Heads: 16 for Q and 2 for KV
Head Dimension: 256
Rotary Position Embedding Dimension: 64
Gated DeltaNet:
**Number of Linear Attention Heads: 32 for V and 16 for QK
**Head Dimension: 128
Mixture of Experts:
**Number of Experts: 308 (uniformly pruned from 512)
**Number of Activated Experts: 10
**Number of Shared Experts: 1
Context Length: 262,144 natively
Compression Method: REAP (Router-weighted Expert Activation Pruning)
Compression Ratio: 40% expert pruning

Test video 1 (agentic task) @Q4_K_XL : https://www.bilibili.com/video/BV1f8cNzcEHV/
Prompt: please clone the repository https://github.com/ggml-org/llama.cpp in /home/lovedheart/llama_ and review the PR 19435.

Test video 2 -> fastllm (int8 quantization) approx. Q8_0 in GGUF : https://www.bilibili.com/video/BV1hwFJzXEVP/
Prompt: Create a cosmic nebula background using Three.js with the following requirements: a deep black space background with twinkling white stars; 2–3 large semi-transparent purple/pink nebula clouds with a smoky texture; slow rotation animation; optimized for white text display. Implementation details: 1. Starfield: 5000 white particles randomly distributed with subtle twinkling; 2. Nebula: 2–3 large purple particle clusters using additive blending mode; 3. Colors: #8B5CF6, #C084FC, #F472B6 (purple to pink gradient); 4. Animation: overall rotation.y += 0.001, stars' opacity flickering; 5. Setup: WebGLRenderer with alpha:true and black background.

Downloads last month: 100

GGUF

Model size

49B params

Architecture

qwen3next

Hardware compatibility

2-bit

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