Instructions to use John1604/Qwen3-Next-80B-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 John1604/Qwen3-Next-80B-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 John1604/Qwen3-Next-80B-A3B-Instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf John1604/Qwen3-Next-80B-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 John1604/Qwen3-Next-80B-A3B-Instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf John1604/Qwen3-Next-80B-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 John1604/Qwen3-Next-80B-A3B-Instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf John1604/Qwen3-Next-80B-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 John1604/Qwen3-Next-80B-A3B-Instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf John1604/Qwen3-Next-80B-A3B-Instruct-gguf:Q4_K_M

Use Docker

docker model run hf.co/John1604/Qwen3-Next-80B-A3B-Instruct-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use John1604/Qwen3-Next-80B-A3B-Instruct-gguf with Ollama:
```
ollama run hf.co/John1604/Qwen3-Next-80B-A3B-Instruct-gguf:Q4_K_M
```

Unsloth Studio

How to use John1604/Qwen3-Next-80B-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 John1604/Qwen3-Next-80B-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 John1604/Qwen3-Next-80B-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 John1604/Qwen3-Next-80B-A3B-Instruct-gguf to start chatting

How to use John1604/Qwen3-Next-80B-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 John1604/Qwen3-Next-80B-A3B-Instruct-gguf:Q4_K_M

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": "John1604/Qwen3-Next-80B-A3B-Instruct-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use John1604/Qwen3-Next-80B-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 John1604/Qwen3-Next-80B-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 "John1604/Qwen3-Next-80B-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"

Docker Model Runner
How to use John1604/Qwen3-Next-80B-A3B-Instruct-gguf with Docker Model Runner:
```
docker model run hf.co/John1604/Qwen3-Next-80B-A3B-Instruct-gguf:Q4_K_M
```

Lemonade

How to use John1604/Qwen3-Next-80B-A3B-Instruct-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull John1604/Qwen3-Next-80B-A3B-Instruct-gguf:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-Next-80B-A3B-Instruct-gguf-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use John1604/Qwen3-Next-80B-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 John1604/Qwen3-Next-80B-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 John1604/Qwen3-Next-80B-A3B-Instruct-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat

Qwen3 Next Instruct gguf

Make sure you have enough memory/GPU

Use the model in ollama

First download and install ollama.

https://ollama.com/download

Note: the official ollama models do not have Qwen3-Next support yet. You need do the following.

Command

in windows command line (or mac os, linux), or in terminal in ubuntu, type:

ollama run hf.co/John1604/Qwen3-Next-80B-A3B-Instruct-gguf:q3_k_m

(q3_k_m is the model quant type, q3_k_s, q4_k_m, ..., can also be used)

C:\Users\developer>ollama run hf.co/John1604/Qwen3-Next-80B-A3B-Instruct-gguf:q3_k_m
pulling manifest
...
writing manifest
success

>>> Send a message (/? for help)

After you run command: ollama run hf.co/John1604/Qwen3-Next-80B-A3B-Instruct-gguf:q3_k_m, it will appear in ollama UI - you may select this model hf.co/John1604/Qwen3-Next-80B-A3B-Instruct-gguf:q3_k_m from the model list, and run it the same way as other ollama supported models.

Use the model in LM Studio

download and install LM Studio

https://lmstudio.ai/

Discover models

In the LM Studio, click "Discover" icon. "Mission Control" popup window will be displayed.

In the "Mission Control" search bar, type "John1604/Qwen3-Next-80B-A3B-Instruct-gguf" and check "GGUF", the model should be found.

Download a quantized model.

Load the quantized model.

Ask questions.

quantized models

Type	Bits	Quality	Description
Q2_K	2-bit	🟥 Low	Minimal footprint; only for tests
Q3_K_S	3-bit	🟧 Low	“Small” variant (less accurate)
Q3_K_M	3-bit	🟧 Low–Med	“Medium” variant
Q4_K_S	4-bit	🟨 Med	Small, faster, slightly less quality
Q4_K_M	4-bit	🟩 Med–High	“Medium” — best 4-bit balance
Q5_K_S	5-bit	🟩 High	Slightly smaller than Q5_K_M
Q5_K_M	5-bit	🟩🟩 High	Excellent general-purpose quant
Q6_K	6-bit	🟩🟩🟩 Very High	Almost FP16 quality, larger size
Q8_0	8-bit	🟩🟩🟩🟩	Near-lossless baseline

Downloads last month: 130

GGUF

Model size

80B params

Architecture

qwen3next

Hardware compatibility

2-bit

3-bit

4-bit

6-bit

8-bit

16-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for John1604/Qwen3-Next-80B-A3B-Instruct-gguf

Base model

Qwen/Qwen3-Next-80B-A3B-Instruct

Quantized

(73)

this model