Instructions to use Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-GGUF",
	filename="Qwen3.5-27B.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-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 Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-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 Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-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 Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-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": "Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-GGUF:Q4_K_M

Ollama
How to use Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-GGUF with Ollama:
```
ollama run hf.co/Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-GGUF:Q4_K_M
```

Unsloth Studio

How to use Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-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 Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-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 Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-GGUF to start chatting

How to use Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-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": "Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-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 Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-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 Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-GGUF with Docker Model Runner:
```
docker model run hf.co/Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-GGUF:Q4_K_M
```

Lemonade

How to use Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-GGUF-Q4_K_M

List all available models

lemonade list

CPU-Optimized Builds — Making this model accessible beyond GPU hardware

by 19440harry - opened Mar 5

Discussion

19440harry

Mar 5

I've been running this model on an RTX 4070 Mobile and the reasoning quality is genuinely impressive, especially for a local 27B. It got me thinking about accessibility for users who don't have a discrete GPU.

The Byteshape project recently demonstrated CPU-viable deployment of Qwen3-Coder-30B-A3B on a Raspberry Pi 5: https://huggingface.co/byteshape/Qwen3-Coder-30B-A3B-Instruct-GGUF

I understand that approach works partly because it's a MoE architecture with only 3B active parameters per forward pass, which is a different challenge from a dense 27B. But with aggressive quantization (Q2/Q3) and CPU-specific kernel optimization there may be a viable path here too.

A CPU-friendly build would open this model to a significantly wider audience, anyone running a mini PC, a home server, a Raspberry Pi cluster, or simply without GPU access. Given the reasoning quality this model delivers, that's a meaningful expansion of who can actually use it.

Questions:

Has CPU deployment been considered or tested?
Are there community members with CPU optimization experience who'd want to collaborate on this?

Would genuinely like to see this model reach more hardware.

kth8

Mar 10

Running dense 27B model on CPU is going to be a bad time no matter what optimizations you try to do. Best model for running on CPU I would say is https://huggingface.co/LiquidAI/LFM2-24B-A2B with 2B active and it's not a reasoning model so you don't need to wait 10 minutes to get a response after every prompt.

19440harry

Mar 10

Running dense 27B model on CPU is going to be a bad time no matter what optimizations you try to do. Best model for running on CPU I would say is https://huggingface.co/LiquidAI/LFM2-24B-A2B with 2B active and it's not a reasoning model so you don't need to wait 10 minutes to get a response after every prompt.

Fair point on the raw performance that a dense 27B on CPU will never be fast. The use case I had in mind is more async or batch workloads where latency isn't the constraint, and the reasoning quality of this fine-tune is the value proposition. The LFM2 recommendation is interesting for interactive use cases though. Has anyone actually benchmarked this model at Q2 on CPU to see where the floor is before writing it off entirely?

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