Instructions to use majentik/Qwen3.5-27B-RotorQuant-GGUF-IQ4_XS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use majentik/Qwen3.5-27B-RotorQuant-GGUF-IQ4_XS 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 majentik/Qwen3.5-27B-RotorQuant-GGUF-IQ4_XS:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf majentik/Qwen3.5-27B-RotorQuant-GGUF-IQ4_XS:IQ4_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf majentik/Qwen3.5-27B-RotorQuant-GGUF-IQ4_XS:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf majentik/Qwen3.5-27B-RotorQuant-GGUF-IQ4_XS:IQ4_XS

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf majentik/Qwen3.5-27B-RotorQuant-GGUF-IQ4_XS:IQ4_XS
# Run inference directly in the terminal:
./build/bin/llama cli -hf majentik/Qwen3.5-27B-RotorQuant-GGUF-IQ4_XS:IQ4_XS

Use Docker

docker model run hf.co/majentik/Qwen3.5-27B-RotorQuant-GGUF-IQ4_XS:IQ4_XS

Llama new
LM Studio
Jan

vLLM

How to use majentik/Qwen3.5-27B-RotorQuant-GGUF-IQ4_XS with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "majentik/Qwen3.5-27B-RotorQuant-GGUF-IQ4_XS"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "majentik/Qwen3.5-27B-RotorQuant-GGUF-IQ4_XS",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/majentik/Qwen3.5-27B-RotorQuant-GGUF-IQ4_XS:IQ4_XS

Ollama
How to use majentik/Qwen3.5-27B-RotorQuant-GGUF-IQ4_XS with Ollama:
```
ollama run hf.co/majentik/Qwen3.5-27B-RotorQuant-GGUF-IQ4_XS:IQ4_XS
```
Unsloth Desktop

How to use majentik/Qwen3.5-27B-RotorQuant-GGUF-IQ4_XS with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf majentik/Qwen3.5-27B-RotorQuant-GGUF-IQ4_XS:IQ4_XS

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "majentik/Qwen3.5-27B-RotorQuant-GGUF-IQ4_XS:IQ4_XS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use majentik/Qwen3.5-27B-RotorQuant-GGUF-IQ4_XS with Docker Model Runner:
```
docker model run hf.co/majentik/Qwen3.5-27B-RotorQuant-GGUF-IQ4_XS:IQ4_XS
```

Lemonade

How to use majentik/Qwen3.5-27B-RotorQuant-GGUF-IQ4_XS with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull majentik/Qwen3.5-27B-RotorQuant-GGUF-IQ4_XS:IQ4_XS

Run and chat with the model

lemonade run user.Qwen3.5-27B-RotorQuant-GGUF-IQ4_XS-IQ4_XS

List all available models

lemonade list

Hermes Agent

How to use majentik/Qwen3.5-27B-RotorQuant-GGUF-IQ4_XS with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf majentik/Qwen3.5-27B-RotorQuant-GGUF-IQ4_XS:IQ4_XS

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 majentik/Qwen3.5-27B-RotorQuant-GGUF-IQ4_XS:IQ4_XS

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use majentik/Qwen3.5-27B-RotorQuant-GGUF-IQ4_XS with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf majentik/Qwen3.5-27B-RotorQuant-GGUF-IQ4_XS:IQ4_XS

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 "majentik/Qwen3.5-27B-RotorQuant-GGUF-IQ4_XS:IQ4_XS" \
  --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"

majentik commited on Sep 15

Commit

af20e55

verified ·

1 Parent(s): e84ccba

docs: card hygiene — llama-completion -no-cnv, drop fork-clone quickstart (upstream-first KV guidance)

Browse files

Files changed (1) hide show

README.md +3 -30

README.md CHANGED Viewed

@@ -57,40 +57,13 @@ This model combines two independent compression techniques:
 ## Quickstart
-### Option A — RotorQuant KV cache (experimental fork — not required)
-You must build from the RotorQuant-enabled llama.cpp fork:
-```bash
-# Clone and build the fork
-git clone https://github.com/johndpope/llama-cpp-turboquant.git
-cd llama-cpp-turboquant && git checkout feature/planarquant-kv-cache
-# CUDA (Windows/Linux)
-cmake -B build -DGGML_CUDA=ON -DCMAKE_BUILD_TYPE=Release && cmake --build build -j
-# Metal (Apple Silicon)
-cmake -B build -DGGML_METAL=ON -DGGML_METAL_EMBED_LIBRARY=ON -DCMAKE_BUILD_TYPE=Release && cmake --build build -j
-# Run with RotorQuant KV cache
-./build/bin/llama-cli -m Qwen3.5-27B-RotorQuant-GGUF-IQ4_XS.gguf \
-  --cache-type-k iso3 --cache-type-v iso3 \
-  -ngl 99 -fa \
-  -p "Explain quantum computing"
-# Or run as a server
-./build/bin/llama-server -m Qwen3.5-27B-RotorQuant-GGUF-IQ4_XS.gguf \
-  --cache-type-k iso3 --cache-type-v iso3 \
-  -ngl 99 -fa --jinja
-```
-### Option B — With standard llama.cpp / LM Studio / Ollama
-The GGUF works as a normal quantised model. You won't get RotorQuant-specific KV cache benefits, but standard KV cache quantization (q8_0, q4_0) still reduces VRAM significantly.
 **llama.cpp (upstream)**
 ```bash
-llama-cli -m Qwen3.5-27B-RotorQuant-GGUF-IQ4_XS.gguf \
   --cache-type-k q8_0 --cache-type-v q8_0 \
   -ngl 99 -fa \
   -p "Explain quantum computing"

 ## Quickstart
+### llama.cpp / LM Studio / Ollama (upstream)
+The GGUF works as a normal quantised model. For KV-cache savings use the upstream path described in the tip at the top of this card (`-ctk q8_0 -ctv q8_0`, or `q4_0`); no fork is required.
 **llama.cpp (upstream)**
 ```bash
+llama-completion -no-cnv -m Qwen3.5-27B-RotorQuant-GGUF-IQ4_XS.gguf \
   --cache-type-k q8_0 --cache-type-v q8_0 \
   -ngl 99 -fa \
   -p "Explain quantum computing"