Instructions to use jackasda211233/Qwen3.6-27B-AEON-RYS-15-20-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use jackasda211233/Qwen3.6-27B-AEON-RYS-15-20-GGUF 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 jackasda211233/Qwen3.6-27B-AEON-RYS-15-20-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf jackasda211233/Qwen3.6-27B-AEON-RYS-15-20-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jackasda211233/Qwen3.6-27B-AEON-RYS-15-20-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf jackasda211233/Qwen3.6-27B-AEON-RYS-15-20-GGUF:BF16

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 jackasda211233/Qwen3.6-27B-AEON-RYS-15-20-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf jackasda211233/Qwen3.6-27B-AEON-RYS-15-20-GGUF:BF16

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 jackasda211233/Qwen3.6-27B-AEON-RYS-15-20-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jackasda211233/Qwen3.6-27B-AEON-RYS-15-20-GGUF:BF16

Use Docker

docker model run hf.co/jackasda211233/Qwen3.6-27B-AEON-RYS-15-20-GGUF:BF16

LM Studio
Jan
Ollama
How to use jackasda211233/Qwen3.6-27B-AEON-RYS-15-20-GGUF with Ollama:
```
ollama run hf.co/jackasda211233/Qwen3.6-27B-AEON-RYS-15-20-GGUF:BF16
```
Unsloth Desktop

How to use jackasda211233/Qwen3.6-27B-AEON-RYS-15-20-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jackasda211233/Qwen3.6-27B-AEON-RYS-15-20-GGUF:BF16

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": "jackasda211233/Qwen3.6-27B-AEON-RYS-15-20-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use jackasda211233/Qwen3.6-27B-AEON-RYS-15-20-GGUF with Docker Model Runner:
```
docker model run hf.co/jackasda211233/Qwen3.6-27B-AEON-RYS-15-20-GGUF:BF16
```

Lemonade

How to use jackasda211233/Qwen3.6-27B-AEON-RYS-15-20-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jackasda211233/Qwen3.6-27B-AEON-RYS-15-20-GGUF:BF16

Run and chat with the model

lemonade run user.Qwen3.6-27B-AEON-RYS-15-20-GGUF-BF16

List all available models

lemonade list

Hermes Agent

How to use jackasda211233/Qwen3.6-27B-AEON-RYS-15-20-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jackasda211233/Qwen3.6-27B-AEON-RYS-15-20-GGUF:BF16

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 jackasda211233/Qwen3.6-27B-AEON-RYS-15-20-GGUF:BF16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use jackasda211233/Qwen3.6-27B-AEON-RYS-15-20-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jackasda211233/Qwen3.6-27B-AEON-RYS-15-20-GGUF:BF16

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 "jackasda211233/Qwen3.6-27B-AEON-RYS-15-20-GGUF:BF16" \
  --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"

jackasda211233 commited on May 6

Commit

ff239f7

verified ·

1 Parent(s): fd7096c

Document current single-GPU Q4NL footprint

Browse files

Files changed (1) hide show

README.md +2 -0

README.md CHANGED Viewed

@@ -90,6 +90,8 @@ For long-context use, the same profile can be run with a larger context. The def
   -c 131072
 ```
 For the later 131k practical canvas-agent comparison, we used FP32 KV as a conservative isolation setting to make sure any failure would not be caused by KV precision:
 ```bash

   -c 131072
 ```
+Current local serving observation: the Q4_NL model line can be exposed as an OpenAI-compatible endpoint such as `http://127.0.0.1:8027/v1`. In our current local setup, roughly `160k` context with default/FP16 KV fits at about `20.3 GiB` observed VRAM and runs comfortably on a single 24 GB-class GPU. Treat this as a practical local footprint note, not a normalized cross-hardware memory benchmark.
 For the later 131k practical canvas-agent comparison, we used FP32 KV as a conservative isolation setting to make sure any failure would not be caused by KV precision:
 ```bash