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"

Qwen3.6-27B-AEON-RYS-15-20-GGUF / README.md

jackasda211233

Link BF16 safetensors and GGUF artifacts in model card

c5fde11 verified 4 months ago

7.41 kB

license: apache-2.0
language:
  - en
  - zh
  - multilingual
tags:
  - gguf
  - qwen3
  - qwen3.6
  - reasoning
  - coding
  - academic-writing
  - uncensored
  - rys
base_model:
  - Qwen/Qwen3.6-27B

Qwen3.6-27B-AEON-RYS-MaxThinkCoder-IQ4_NL GGUF

Hyper-focused Q4NL RYS release for:

programming
technical reasoning
academic-style writing

This release is built from:

AEON source model: https://huggingface.co/AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored

Use this model with:

custom ik-llama fork, specialized and tuned for this exact model: https://github.com/noonr48/qwen36-aeon-ik-llama

Side note (tool calling): some prompts can trigger repeated identical tool calls in one assistant turn (especially when the tool result is empty / slow). Update to the latest ik-llama fork version: it now deduplicates identical tool_calls server-side.

At a glance

released Q4_NL GGUF: Qwen3.6-27B-AEON-RYS-MaxThinkCoder-IQ4_NL-ik-llama-custom-mixed.gguf
BF16 GGUF reference: Qwen3.6-27B-AEON-RYS-MaxThinkCoder-BF16.gguf
HF-format BF16 safetensors: bf16-safetensors/
intended runtime: custom ik-llama
compression: 54G BF16 -> 16G IQ4_NL
mixed validation snapshot: 0.7299 BF16 -> 0.7244 IQ4_NL
overall performance change: -0.0055 absolute, about -0.75% relative
public codename: MaxThinkCoder
project focus: get as much capability as possible out of a Q4-class RYS model for hard English-first work
not a generic chat release
not a stock llama.cpp release

BF16 GGUF

A BF16 GGUF is also included for people who want the unquantized GGUF-side artifact from the same released 15,20 RYS branch:

Qwen3.6-27B-AEON-RYS-MaxThinkCoder-BF16.gguf

Use this if you want a GGUF reference build, local conversion/testing, or to compare quantization behavior against the released IQ4_NL GGUF. For normal inference, the IQ4_NL file is the practical target. For Transformers/LoRA/SFT workflows, use the bf16-safetensors/ folder instead.

Size note:

BF16 GGUF: about 54G
IQ4_NL GGUF: about 16G

BF16 vs released custom IQ4_NL

This is the critical compression result for the released custom 15,20 branch:

BF16 size: 54G
released IQ4_NL size: 16G
mixed 4-probe mean: 0.7299 BF16 -> 0.7244 IQ4_NL
net performance change: -0.0055 absolute, about -0.75% relative

Probe-level snapshot:

probe	BF16	IQ4_NL
`math_16`	`0.8421`	`0.7897`
`eq_16`	`0.7123`	`0.7111`
`math_4`	`0.4851`	`0.5170`
`gsm8k_5`	`0.8800`	`0.8800`

Practical read:

the released Q4 model is roughly 70% smaller on disk
the mixed validation snapshot stayed under a 1% overall drop
eq_16 and gsm8k_5 were effectively flat
math_4 did not regress in this tiny probe
the real measurable hit was mostly on math_16

Speed snapshot

Exact comparison hardware:

6x NVIDIA GeForce RTX 5060 Ti

runtime	tested file	ctx	np	KV	decode tok/s	prompt tok/s	note
patched upstream-style `llama.cpp`	same internal standard-typed comparison file	`4096`	`1`	`f16`	`22.51`	`187.18`	internal comparison only
custom `ik-llama` fork	released custom-mixed file	`409600`	`2`	`f32/f32`	`39.37`	`164.98`	actual deployment target

Why there is no `llama.cpp` file in this release

We did build and benchmark an internal standard-typed comparison artifact. We are not releasing it as a public llama.cpp file.

Why:

the main model this project is about is the custom mixed GGUF, which needs the forked ik-llama runtime
even the internal standard-typed path was only validated on a patched upstream-style llama.cpp, not clean stock mainline
since users would still need a special runtime path anyway, we did not think it was worth shipping a second public file that suggests plain stock llama.cpp support

So the intended reading is simple:

this repo releases the ik-llama-targeted model
if you want plain stock llama.cpp, this is not that release

Hyper-focused project

This was a deliberately narrow project.

The target was not “best general chat model”. The target was:

strongest Q4-class English-first model we could get for coding, reasoning, and academic work
using the AEON uncensored branch as the source
using the custom ik-llama path because prior RYS experiments suggested that path preserved quality better than standard llama.cpp-style quantization

Imatrix calibration profile

The quantization was deliberately biased toward reasoning and technical work.

Heuristic calibration breakdown:

math_reasoning: 5,688 chunks, 1,706,070 chars (36.0%)
code_technical: 3,518 chunks, 1,343,392 chars (28.4%)
experiment_docs: 808 chunks, 224,169 chars (4.7%)
writing_chat: 387 chunks, 164,097 chars (3.5%)
other: 5,139 chunks, 1,249,396 chars (26.4%)

Practical read:

heavy focus on reasoning math, code, technical prose, and experiment artifacts
very little emphasis on generic social chat

RYS choice

This release came from the AEON-derived 15,20 RYS branch. That was the practical release target because it quantized cleanly and held up as the best balanced candidate for this experiment.

Use case

Recommended:

coding
technical reasoning
academic-style writing
long-context English work

Not recommended as a generic safe-default chat model. This branch came from an uncensored source path.

BF16 safetensors for fine-tuning

The original HF-format BF16 checkpoint for the released 15,20 RYS branch is included here:

bf16-safetensors/

Use the files in that folder for Transformers-based work such as LoRA, SFT, continued training, or conversion into another training format. Use the GGUF file in the repo root for ik-llama inference.

Loading example:

from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "jackasda211233/Qwen3.6-27B-AEON-RYS-15-20-GGUF"
subfolder = "bf16-safetensors"

tok = AutoTokenizer.from_pretrained(repo_id, subfolder=subfolder, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo_id,
    subfolder=subfolder,
    torch_dtype="bfloat16",
    device_map="auto",
    trust_remote_code=True,
)

RYS note for anyone rebuilding or modifying this checkpoint: the config is part of the model. The BF16 folder keeps the corrected hybrid-stack metadata for the 15,20 insert, including text_config.num_hidden_layers = 69 and a 69-entry text_config.layer_types list. Do not change the layer count without remapping layer_types to the same layer order as the tensors.

Qwen3.6-27B-AEON-RYS-MaxThinkCoder-IQ4_NL GGUF

At a glance

BF16 GGUF

BF16 vs released custom IQ4_NL

Speed snapshot

Why there is no llama.cpp file in this release

Hyper-focused project

Imatrix calibration profile

RYS choice

Use case

BF16 safetensors for fine-tuning

Why there is no `llama.cpp` file in this release