Instructions to use timteh673/Qwen3.5-122B-A10B-Opus-Reasoning-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use timteh673/Qwen3.5-122B-A10B-Opus-Reasoning-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="timteh673/Qwen3.5-122B-A10B-Opus-Reasoning-GGUF",
	filename="Qwen3.5-122B-A10B-Opus-Reasoning-BF16.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 timteh673/Qwen3.5-122B-A10B-Opus-Reasoning-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf timteh673/Qwen3.5-122B-A10B-Opus-Reasoning-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf timteh673/Qwen3.5-122B-A10B-Opus-Reasoning-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf timteh673/Qwen3.5-122B-A10B-Opus-Reasoning-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf timteh673/Qwen3.5-122B-A10B-Opus-Reasoning-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 timteh673/Qwen3.5-122B-A10B-Opus-Reasoning-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf timteh673/Qwen3.5-122B-A10B-Opus-Reasoning-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 timteh673/Qwen3.5-122B-A10B-Opus-Reasoning-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf timteh673/Qwen3.5-122B-A10B-Opus-Reasoning-GGUF:Q4_K_M

Use Docker

docker model run hf.co/timteh673/Qwen3.5-122B-A10B-Opus-Reasoning-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use timteh673/Qwen3.5-122B-A10B-Opus-Reasoning-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "timteh673/Qwen3.5-122B-A10B-Opus-Reasoning-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": "timteh673/Qwen3.5-122B-A10B-Opus-Reasoning-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/timteh673/Qwen3.5-122B-A10B-Opus-Reasoning-GGUF:Q4_K_M

Ollama
How to use timteh673/Qwen3.5-122B-A10B-Opus-Reasoning-GGUF with Ollama:
```
ollama run hf.co/timteh673/Qwen3.5-122B-A10B-Opus-Reasoning-GGUF:Q4_K_M
```

Unsloth Studio

How to use timteh673/Qwen3.5-122B-A10B-Opus-Reasoning-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 timteh673/Qwen3.5-122B-A10B-Opus-Reasoning-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 timteh673/Qwen3.5-122B-A10B-Opus-Reasoning-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for timteh673/Qwen3.5-122B-A10B-Opus-Reasoning-GGUF to start chatting

How to use timteh673/Qwen3.5-122B-A10B-Opus-Reasoning-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf timteh673/Qwen3.5-122B-A10B-Opus-Reasoning-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": "timteh673/Qwen3.5-122B-A10B-Opus-Reasoning-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use timteh673/Qwen3.5-122B-A10B-Opus-Reasoning-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 timteh673/Qwen3.5-122B-A10B-Opus-Reasoning-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 timteh673/Qwen3.5-122B-A10B-Opus-Reasoning-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use timteh673/Qwen3.5-122B-A10B-Opus-Reasoning-GGUF with Docker Model Runner:
```
docker model run hf.co/timteh673/Qwen3.5-122B-A10B-Opus-Reasoning-GGUF:Q4_K_M
```

Lemonade

How to use timteh673/Qwen3.5-122B-A10B-Opus-Reasoning-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull timteh673/Qwen3.5-122B-A10B-Opus-Reasoning-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-122B-A10B-Opus-Reasoning-GGUF-Q4_K_M

List all available models

lemonade list

Uncensored?

by NeedAHugNOW - opened Apr 14

Discussion

NeedAHugNOW

Apr 14

In the model card, it is stated that the model provides uncensored output. But it doesn't seem so. It clearly wastes a lot of tokens just like the original model after every few steps inside the thinking stage. It really doesn't seem much different from the base model in this regard.

HauHauCS model appears to be truly uncensored. So was uncensoring one of the focused parts of this release or the primary goal was just to learn on opus data?

Tested with llama.cpp and Q4_K_M quant and BF16 mmproj from unsloth. This model also seems to strongly think it doesn't have vision capabilities, and again wastes a bunch of token during thinking process arguing with itself like: "I'm text model, I dont have vision, must hallucinate some details to reply, wait, analyzing the image xyz...."

InformaticsSolutions

Apr 18

Same here. Tried the Q5 version which works, otherwise, great. However, it is still heavily censored (medical questions, ethics, end of life, etc) and obstinately refuses to respond directly.
Would it be a lot to ask to consider heretic-ing it as well? Thanks a lot!

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