Instructions to use lefromage/Qwen3-Next-80B-A3B-Instruct-split-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lefromage/Qwen3-Next-80B-A3B-Instruct-split-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="lefromage/Qwen3-Next-80B-A3B-Instruct-split-GGUF",
	filename="Qwen3-Next-80B-A3B-Instruct-IQ4_XS-00001-of-00012.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use lefromage/Qwen3-Next-80B-A3B-Instruct-split-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf lefromage/Qwen3-Next-80B-A3B-Instruct-split-GGUF:Q4_K_S
# Run inference directly in the terminal:
llama-cli -hf lefromage/Qwen3-Next-80B-A3B-Instruct-split-GGUF:Q4_K_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf lefromage/Qwen3-Next-80B-A3B-Instruct-split-GGUF:Q4_K_S
# Run inference directly in the terminal:
llama-cli -hf lefromage/Qwen3-Next-80B-A3B-Instruct-split-GGUF:Q4_K_S

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 lefromage/Qwen3-Next-80B-A3B-Instruct-split-GGUF:Q4_K_S
# Run inference directly in the terminal:
./llama-cli -hf lefromage/Qwen3-Next-80B-A3B-Instruct-split-GGUF:Q4_K_S

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 lefromage/Qwen3-Next-80B-A3B-Instruct-split-GGUF:Q4_K_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lefromage/Qwen3-Next-80B-A3B-Instruct-split-GGUF:Q4_K_S

Use Docker

docker model run hf.co/lefromage/Qwen3-Next-80B-A3B-Instruct-split-GGUF:Q4_K_S

LM Studio
Jan

vLLM

How to use lefromage/Qwen3-Next-80B-A3B-Instruct-split-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lefromage/Qwen3-Next-80B-A3B-Instruct-split-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lefromage/Qwen3-Next-80B-A3B-Instruct-split-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lefromage/Qwen3-Next-80B-A3B-Instruct-split-GGUF:Q4_K_S

Ollama
How to use lefromage/Qwen3-Next-80B-A3B-Instruct-split-GGUF with Ollama:
```
ollama run hf.co/lefromage/Qwen3-Next-80B-A3B-Instruct-split-GGUF:Q4_K_S
```

Unsloth Studio

How to use lefromage/Qwen3-Next-80B-A3B-Instruct-split-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 lefromage/Qwen3-Next-80B-A3B-Instruct-split-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 lefromage/Qwen3-Next-80B-A3B-Instruct-split-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for lefromage/Qwen3-Next-80B-A3B-Instruct-split-GGUF to start chatting

How to use lefromage/Qwen3-Next-80B-A3B-Instruct-split-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf lefromage/Qwen3-Next-80B-A3B-Instruct-split-GGUF:Q4_K_S

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": "lefromage/Qwen3-Next-80B-A3B-Instruct-split-GGUF:Q4_K_S"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use lefromage/Qwen3-Next-80B-A3B-Instruct-split-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 lefromage/Qwen3-Next-80B-A3B-Instruct-split-GGUF:Q4_K_S

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 lefromage/Qwen3-Next-80B-A3B-Instruct-split-GGUF:Q4_K_S

Run Hermes

hermes

Docker Model Runner
How to use lefromage/Qwen3-Next-80B-A3B-Instruct-split-GGUF with Docker Model Runner:
```
docker model run hf.co/lefromage/Qwen3-Next-80B-A3B-Instruct-split-GGUF:Q4_K_S
```

Lemonade

How to use lefromage/Qwen3-Next-80B-A3B-Instruct-split-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lefromage/Qwen3-Next-80B-A3B-Instruct-split-GGUF:Q4_K_S

Run and chat with the model

lemonade run user.Qwen3-Next-80B-A3B-Instruct-split-GGUF-Q4_K_S

List all available models

lemonade list

How to use from the

Use from the

llama-cpp-python library

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="lefromage/Qwen3-Next-80B-A3B-Instruct-split-GGUF",
	filename="",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

GGUFs split in ~5GB chunks

build/bin/llama-cli -hf lefromage/Qwen3-Next-80B-A3B-Instruct-split-GGUF:Q2_K --prompt 'What is the capital of France?' --no-mmap -st

Another way to download the Q2_K quant model pieces:

pip install hf_transfer 'huggingface_hub[cli]'

time hf download lefromage/Qwen3-Next-80B-A3B-Instruct-split-GGUF --include "*Q2_K*.gguf" --local-dir Q2_K

build/bin/llama-cli -m Q2_K/Qwen3-Next-80B-A3B-Instruct-Q2_K-00001-of-*.gguf --no-mmap --prompt 'what is the capital of france' -st

check https://huggingface.co/lefromage/Qwen3-Next-80B-A3B-Instruct-GGUF for more details

currently getting 6 tokens per second for generation for simple prompt:

time build/bin/llama-cli -hf lefromage/Qwen3-Next-80B-A3B-Instruct-split-GGUF:Q2_K --no-mmap --prompt 'explain quantum computing in a paragraph' -st

...

user
explain quantum computing in a paragraph
assistant
Quantum computing is a revolutionary approach to computation that leverages the principles of quantum mechanics—such as superposition, entanglement, and interference—to process information in fundamentally different ways than classical computers. Instead of using binary bits (0 or 1), quantum computers use quantum bits, or qubits, which can exist in a combination of 0 and 1 simultaneously thanks to superposition. This allows a quantum computer to explore many possible solutions at once. When qubits become entangled, their states become interdependent, meaning the state of one instantly influences the other, even at a distance. By manipulating these qubits with precise microwave or laser pulses, quantum algorithms can solve certain problems—like factoring large numbers, simulating molecules, or optimizing complex systems—exponentially faster than classical computers. While still in early development and highly sensitive to environmental noise, quantum computing holds the potential to transform fields like cryptography, drug discovery, artificial intelligence, and financial modeling. [end of text]


llama_perf_sampler_print:    sampling time =      13.05 ms /   210 runs   (    0.06 ms per token, 16093.19 tokens per second)
llama_perf_context_print:        load time =   12190.98 ms

llama_perf_context_print: prompt eval time =    5201.06 ms /    14 tokens (  371.50 ms per token,     2.69 tokens per second)
llama_perf_context_print:        eval time =   31579.94 ms /   195 runs   (  161.95 ms per token,     6.17 tokens per second)
llama_perf_context_print:       total time =   36857.21 ms /   209 tokens

llama_perf_context_print:    graphs reused =          0
llama_memory_breakdown_print: | memory breakdown [MiB]   | total    free     self   model   context   compute    unaccounted |
llama_memory_breakdown_print: |   - Metal (Apple M4 Max) | 98304 = 70034 + (28151 = 27675 +     171 +     304) +         117 |
llama_memory_breakdown_print: |   - Host                 |                    167 =    97 +       0 +      70                |
ggml_metal_free: deallocating

Downloads last month: 50

GGUF

Model size

80B params

Architecture

qwen3next

Hardware compatibility

2-bit

3-bit

4-bit

Model tree for lefromage/Qwen3-Next-80B-A3B-Instruct-split-GGUF

Base model

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

Quantized

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