Instructions to use lovedheart/Qwen3-Next-REAP-60B-A3B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lovedheart/Qwen3-Next-REAP-60B-A3B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="lovedheart/Qwen3-Next-REAP-60B-A3B-Instruct-GGUF",
	filename="Qwen3-Next-REAP-60B-A3B-Q2_K.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use lovedheart/Qwen3-Next-REAP-60B-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 lovedheart/Qwen3-Next-REAP-60B-A3B-Instruct-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf lovedheart/Qwen3-Next-REAP-60B-A3B-Instruct-GGUF:Q2_K

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-60B-A3B-Instruct-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf lovedheart/Qwen3-Next-REAP-60B-A3B-Instruct-GGUF:Q2_K

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 lovedheart/Qwen3-Next-REAP-60B-A3B-Instruct-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf lovedheart/Qwen3-Next-REAP-60B-A3B-Instruct-GGUF:Q2_K

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 lovedheart/Qwen3-Next-REAP-60B-A3B-Instruct-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lovedheart/Qwen3-Next-REAP-60B-A3B-Instruct-GGUF:Q2_K

Use Docker

docker model run hf.co/lovedheart/Qwen3-Next-REAP-60B-A3B-Instruct-GGUF:Q2_K

LM Studio
Jan
Ollama
How to use lovedheart/Qwen3-Next-REAP-60B-A3B-Instruct-GGUF with Ollama:
```
ollama run hf.co/lovedheart/Qwen3-Next-REAP-60B-A3B-Instruct-GGUF:Q2_K
```

Unsloth Studio

How to use lovedheart/Qwen3-Next-REAP-60B-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 lovedheart/Qwen3-Next-REAP-60B-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 lovedheart/Qwen3-Next-REAP-60B-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 lovedheart/Qwen3-Next-REAP-60B-A3B-Instruct-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use lovedheart/Qwen3-Next-REAP-60B-A3B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/lovedheart/Qwen3-Next-REAP-60B-A3B-Instruct-GGUF:Q2_K
```

Lemonade

How to use lovedheart/Qwen3-Next-REAP-60B-A3B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lovedheart/Qwen3-Next-REAP-60B-A3B-Instruct-GGUF:Q2_K

Run and chat with the model

lemonade run user.Qwen3-Next-REAP-60B-A3B-Instruct-GGUF-Q2_K

List all available models

lemonade list

Qwen3-Next-REAP-60B-A3B-Instruct has the following specifications:

Type: Causal Language Models
Number of Parameters: 60B 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: 384 (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: 25% expert pruning

Downloads last month: 121

GGUF

Model size

60B params

Architecture

qwen3next

Hardware compatibility

2-bit

Inference Providers NEW

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Model tree for lovedheart/Qwen3-Next-REAP-60B-A3B-Instruct-GGUF

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Quantized

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