Instructions to use Beinsezii/Qwen3.5-122B-A10B-GGUF-HALO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Beinsezii/Qwen3.5-122B-A10B-GGUF-HALO with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Beinsezii/Qwen3.5-122B-A10B-GGUF-HALO",
	filename="imatrix.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 Beinsezii/Qwen3.5-122B-A10B-GGUF-HALO with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Beinsezii/Qwen3.5-122B-A10B-GGUF-HALO:F16
# Run inference directly in the terminal:
llama-cli -hf Beinsezii/Qwen3.5-122B-A10B-GGUF-HALO:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Beinsezii/Qwen3.5-122B-A10B-GGUF-HALO:F16
# Run inference directly in the terminal:
llama-cli -hf Beinsezii/Qwen3.5-122B-A10B-GGUF-HALO:F16

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 Beinsezii/Qwen3.5-122B-A10B-GGUF-HALO:F16
# Run inference directly in the terminal:
./llama-cli -hf Beinsezii/Qwen3.5-122B-A10B-GGUF-HALO:F16

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 Beinsezii/Qwen3.5-122B-A10B-GGUF-HALO:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Beinsezii/Qwen3.5-122B-A10B-GGUF-HALO:F16

Use Docker

docker model run hf.co/Beinsezii/Qwen3.5-122B-A10B-GGUF-HALO:F16

LM Studio
Jan
Ollama
How to use Beinsezii/Qwen3.5-122B-A10B-GGUF-HALO with Ollama:
```
ollama run hf.co/Beinsezii/Qwen3.5-122B-A10B-GGUF-HALO:F16
```

Unsloth Studio

How to use Beinsezii/Qwen3.5-122B-A10B-GGUF-HALO 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 Beinsezii/Qwen3.5-122B-A10B-GGUF-HALO 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 Beinsezii/Qwen3.5-122B-A10B-GGUF-HALO to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Beinsezii/Qwen3.5-122B-A10B-GGUF-HALO to start chatting

How to use Beinsezii/Qwen3.5-122B-A10B-GGUF-HALO with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Beinsezii/Qwen3.5-122B-A10B-GGUF-HALO:F16

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": "Beinsezii/Qwen3.5-122B-A10B-GGUF-HALO:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Beinsezii/Qwen3.5-122B-A10B-GGUF-HALO with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Beinsezii/Qwen3.5-122B-A10B-GGUF-HALO:F16

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 Beinsezii/Qwen3.5-122B-A10B-GGUF-HALO:F16

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use Beinsezii/Qwen3.5-122B-A10B-GGUF-HALO with Docker Model Runner:
```
docker model run hf.co/Beinsezii/Qwen3.5-122B-A10B-GGUF-HALO:F16
```

Lemonade

How to use Beinsezii/Qwen3.5-122B-A10B-GGUF-HALO with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Beinsezii/Qwen3.5-122B-A10B-GGUF-HALO:F16

Run and chat with the model

lemonade run user.Qwen3.5-122B-A10B-GGUF-HALO-F16

List all available models

lemonade list

(2026/05/30) Now with MTP!

Quant optimized for quality / speed on a Strix Halo 128GiB system. Possibly also beneficial on DGX Spark and similar systems.

The TL;DR is this quant achieves both superior quality and speed compared to homogenous Q6_K.

Depending on your TTM settings you should be between 100k and 200k ctx, or more if you disable vision.

This quant, build 8245 (2026/03/08)

model	size	params	backend	ngl	n_batch	n_ubatch	fa	test	t/s
qwen35moe 122B.A10B Q4_1	94.79 GiB	122.11 B	ROCm	999	1024	1024	1	pp2048	274.99 ± 0.00
qwen35moe 122B.A10B Q4_1	94.79 GiB	122.11 B	ROCm	999	1024	1024	1	tg256	16.62 ± 0.00
qwen35moe 122B.A10B Q4_1	94.79 GiB	122.11 B	ROCm	999	1024	1024	1	pp2048 @ d8192	238.78 ± 0.00
qwen35moe 122B.A10B Q4_1	94.79 GiB	122.11 B	ROCm	999	1024	1024	1	tg256 @ d8192	16.68 ± 0.00