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 Jun 30

Commit

e8cc7cd

verified ·

1 Parent(s): a00669c

Add prominent vision banner with clickable mmproj download links

Browse files

Files changed (1) hide show

README.md +17 -6

README.md CHANGED Viewed

@@ -23,6 +23,10 @@ base_model:
 # Qwen3.6-27B AEON RYS MaxThinkCoder IQ4_NL GGUF for ik-llama
 > Hard runtime requirement: use the custom AEON ik-llama fork:
 >
 > https://github.com/noonr48/qwen36-aeon-ik-llama
@@ -32,24 +36,31 @@ base_model:
 > **Newer fine-tunes built on this base — [SignalLatch](https://huggingface.co/jackasda211233/Qwen3.6-27B-AEON-RYS-SignalLatch-GGUF) (a behaviour fine-tune) and [PatchCode](https://huggingface.co/jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF) (an agentic-coder distil on top of SignalLatch).** This repo is the non-finetuned base.
 ## Vision Support (mmproj)
-These models support image input. Qwen3.6-27B is natively a vision-language model — the included **mmproj** (multimodal projector) files enable image understanding when used with the `--mmproj` flag in llama.cpp / ik-llama.
 The projector is extracted from the official [Qwen/Qwen3.6-27B](https://huggingface.co/Qwen/Qwen3.6-27B) base model. Since text fine-tuning does not modify the vision encoder, one projector works across all three RYS variants (base, SignalLatch, PatchCode).
-| File | Precision | Size | Use |
 |---|---|---:|---|
-| `mmproj-Qwen3.6-27B-base-f32.gguf` | F32 (full precision) | 1.8 GB | Maximum accuracy, most VRAM |
-| `mmproj-Qwen3.6-27B-base-f16.gguf` | F16 (half precision) | 885 MB | **Recommended** — best balance |
-| `mmproj-Qwen3.6-27B-base-q8_0.gguf` | Q8_0 (8-bit quantized) | 601 MB | Smallest, minimal quality loss |
 ### Usage
 Add `--mmproj` to your llama-server command:
 ```bash
-./build/bin/llama-server -m Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode.IQ4_NL.gguf   --mmproj mmproj-Qwen3.6-27B-base-f16.gguf   --jinja -ngl 999 -c 200000
 ```
 Then send images via the standard OpenAI-compatible API:

 # Qwen3.6-27B AEON RYS MaxThinkCoder IQ4_NL GGUF for ik-llama
+> **👁️ Vision Support Added** — This model now supports image input! Download a [mmproj projector file](#vision-support-mmproj) from the file list and add `--mmproj` to enable vision. See the [Vision Support section](#vision-support-mmproj) below for details.
 > Hard runtime requirement: use the custom AEON ik-llama fork:
 >
 > https://github.com/noonr48/qwen36-aeon-ik-llama
 > **Newer fine-tunes built on this base — [SignalLatch](https://huggingface.co/jackasda211233/Qwen3.6-27B-AEON-RYS-SignalLatch-GGUF) (a behaviour fine-tune) and [PatchCode](https://huggingface.co/jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF) (an agentic-coder distil on top of SignalLatch).** This repo is the non-finetuned base.
 ## Vision Support (mmproj)
+> **This model supports vision/image input.** Qwen3.6-27B is natively a vision-language model. Download one of the mmproj (multimodal projector) files below and pass it with `--mmproj` to enable image understanding.
 The projector is extracted from the official [Qwen/Qwen3.6-27B](https://huggingface.co/Qwen/Qwen3.6-27B) base model. Since text fine-tuning does not modify the vision encoder, one projector works across all three RYS variants (base, SignalLatch, PatchCode).
+### Download a projector
+| File | Precision | Size | Link |
 |---|---|---:|---|
+| `mmproj-Qwen3.6-27B-base-f32.gguf` | F32 (full precision) | 1.8 GB | [⬇ Download](https://huggingface.co/jackasda211233/Qwen3.6-27B-AEON-RYS-15-20-GGUF/resolve/main/mmproj-Qwen3.6-27B-base-f32.gguf) |
+| `mmproj-Qwen3.6-27B-base-f16.gguf` | F16 (half precision) | 885 MB | [⬇ Download](https://huggingface.co/jackasda211233/Qwen3.6-27B-AEON-RYS-15-20-GGUF/resolve/main/mmproj-Qwen3.6-27B-base-f16.gguf) |
+| `mmproj-Qwen3.6-27B-base-q8_0.gguf` | Q8_0 (8-bit quantized) | 601 MB | [⬇ Download](https://huggingface.co/jackasda211233/Qwen3.6-27B-AEON-RYS-15-20-GGUF/resolve/main/mmproj-Qwen3.6-27B-base-q8_0.gguf) |
+**Recommended:** `mmproj-Qwen3.6-27B-base-f16.gguf` — best balance of quality and size.
 ### Usage
 Add `--mmproj` to your llama-server command:
 ```bash
+./build/bin/llama-server -m Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode.IQ4_NL.gguf \
+  --mmproj mmproj-Qwen3.6-27B-base-f16.gguf \
+  --jinja -ngl 999 -c 200000
 ```
 Then send images via the standard OpenAI-compatible API: