Instructions to use lovedheart/Qwen3-Next-REAP-20B-A3B-Thinking-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 lovedheart/Qwen3-Next-REAP-20B-A3B-Thinking-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 lovedheart/Qwen3-Next-REAP-20B-A3B-Thinking-GGUF:Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf lovedheart/Qwen3-Next-REAP-20B-A3B-Thinking-GGUF:Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf lovedheart/Qwen3-Next-REAP-20B-A3B-Thinking-GGUF:Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf lovedheart/Qwen3-Next-REAP-20B-A3B-Thinking-GGUF:Q4_K_XL

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf lovedheart/Qwen3-Next-REAP-20B-A3B-Thinking-GGUF:Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama cli -hf lovedheart/Qwen3-Next-REAP-20B-A3B-Thinking-GGUF:Q4_K_XL

Use Docker

docker model run hf.co/lovedheart/Qwen3-Next-REAP-20B-A3B-Thinking-GGUF:Q4_K_XL

Llama new
LM Studio
Jan
Ollama
How to use lovedheart/Qwen3-Next-REAP-20B-A3B-Thinking-GGUF with Ollama:
```
ollama run hf.co/lovedheart/Qwen3-Next-REAP-20B-A3B-Thinking-GGUF:Q4_K_XL
```
Unsloth Desktop

How to use lovedheart/Qwen3-Next-REAP-20B-A3B-Thinking-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf lovedheart/Qwen3-Next-REAP-20B-A3B-Thinking-GGUF:Q4_K_XL

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": "lovedheart/Qwen3-Next-REAP-20B-A3B-Thinking-GGUF:Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use lovedheart/Qwen3-Next-REAP-20B-A3B-Thinking-GGUF with Docker Model Runner:
```
docker model run hf.co/lovedheart/Qwen3-Next-REAP-20B-A3B-Thinking-GGUF:Q4_K_XL
```

Lemonade

How to use lovedheart/Qwen3-Next-REAP-20B-A3B-Thinking-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lovedheart/Qwen3-Next-REAP-20B-A3B-Thinking-GGUF:Q4_K_XL

Run and chat with the model

lemonade run user.Qwen3-Next-REAP-20B-A3B-Thinking-GGUF-Q4_K_XL

List all available models

lemonade list

Hermes Agent

How to use lovedheart/Qwen3-Next-REAP-20B-A3B-Thinking-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 lovedheart/Qwen3-Next-REAP-20B-A3B-Thinking-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 lovedheart/Qwen3-Next-REAP-20B-A3B-Thinking-GGUF:Q4_K_XL

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use lovedheart/Qwen3-Next-REAP-20B-A3B-Thinking-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf lovedheart/Qwen3-Next-REAP-20B-A3B-Thinking-GGUF:Q4_K_XL

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 "lovedheart/Qwen3-Next-REAP-20B-A3B-Thinking-GGUF:Q4_K_XL" \
  --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-Next-REAP-20B-A3B-Thinking-GGUF

File size: 1,231 Bytes

46a9a27

---
base_model:
- Qwen/Qwen3-Next-80B-A3B-Thinking
tags:
- text-generation-inference
license: apache-2.0
---



![qwen3-next-thinking](https://cdn-uploads.huggingface.co/production/uploads/68121d80da035a609e569a81/tXHS7ClRRpoA4sZ2qPegd.png)


**Qwen3-Next-REAP-20B-A3B-Thinking** has the following specifications:

- **Type:** Causal Language Models
- **Number of Parameters**: 20B 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: 128 (uniformly pruned from 512)
- **Number of Activated Experts: 10
- **Number of Shared Experts: 1
- **Context Length**: 262,144 natively and extensible up to 1,010,000 tokens
- **Compression Method**: REAP (Router-weighted Expert Activation Pruning)
- **Compression Ratio**: 75% expert pruning
- **Specialized**: Math, Physics, Control Engineering, Scientific Writing