Instructions to use jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-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-Agentic-Coder-PatchCode-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-Agentic-Coder-PatchCode-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-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-Agentic-Coder-PatchCode-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-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-Agentic-Coder-PatchCode-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-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-Agentic-Coder-PatchCode-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF:BF16

Use Docker

docker model run hf.co/jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF:BF16

LM Studio
Jan
Ollama
How to use jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF with Ollama:
```
ollama run hf.co/jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF:BF16
```

Unsloth Studio

How to use jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-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 jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-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 jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF to start chatting

How to use jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-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-Agentic-Coder-PatchCode-GGUF:BF16

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": "jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-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-Agentic-Coder-PatchCode-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-Agentic-Coder-PatchCode-GGUF:BF16

Run Hermes

hermes

OpenClaw new

How to use jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-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-Agentic-Coder-PatchCode-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-Agentic-Coder-PatchCode-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"

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

Lemonade

How to use jackasda211233/Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF with Lemonade:

Pull the model

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

Run and chat with the model

lemonade run user.Qwen3.6-27B-AEON-RYS-Agentic-Coder-PatchCode-GGUF-BF16

List all available models

lemonade list

Atomic Chat

jackasda211233 commited on Jun 28

Commit

63d0a76

verified ·

1 Parent(s): 09d8abb

Upload README.md with huggingface_hub

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README.md +30 -11

README.md CHANGED Viewed

@@ -181,20 +181,39 @@ Autonomy-loop detail (`agent_loop_eval`, 8 held-out tasks × 5 seeds):
 Non-discriminating (0pp spread): the action-first, verify-before-claim discipline is preserved across all quants.
-## BF16 vs released IQ4_NL
-For this release:
-| item | value |
-|---|---|
-| BF16 size | `57.6 G` |
-| released IQ4_NL size | `16.6 G` |
-| build (5-seed) | `0.867` BF16 → `0.920` IQ4_NL |
-| long-context | `0.942` BF16 → `0.975` IQ4_NL |
-Short read:
-- about `70%` smaller on disk
-- IQ4_NL at or above BF16 on every practical axis (tied within run-to-run noise — not a quality cliff)
 ## Why no stock `llama.cpp` / `vLLM` file

 Non-discriminating (0pp spread): the action-first, verify-before-claim discipline is preserved across all quants.
+## IQ4_NL vs BF16 — head-to-head (5-seed, same-condition)
+The released `IQ4_NL` is what most people should run. It was confirmed against the `BF16` control on the same harness, same seeds:
+| axis | **PatchCode IQ4_NL** | BF16 (control) | read |
+|---|---:|---:|---|
+| build (KritaLite, 5-seed) | `0.920` (±0.067) | `0.867` | IQ4_NL nominally higher; gap `0.053` ≈ within the ±0.067 noise floor |
+| long-context (4 tasks) | `0.975` | `0.942` | both near ceiling; IQ4_NL marginally higher |
+| discipline (action-first style, 5-seed) | `0.842` (±0.333) | `0.931` | BF16 nominally higher; gap `0.089` ≪ ±0.333 spread → within noise |
+| autonomous-loop convergence | `100%` (40/40), 0% halluc/stall | — | preserved |
+| **size on disk** | **`16.6 G`** | `57.6 G` | **IQ4_NL is ~71% smaller** |
+Read:
+- On every behavioural axis the two are **tied within run-to-run noise** (build ±0.067–0.13, discipline ±0.3 on this suite). IQ4_NL is **not** a quality cliff below BF16 — it tracks or edges it within noise.
+- Since behaviour is tied, the decision falls to non-noise axes, where IQ4_NL wins decisively: ~71% smaller, and the plain-quant recipe (no promotion risk).
+- Use `BF16` only as the source-quality reference for re-quantization or downstream work, not as the runtime target.
+## Why there is no Q8 release
+A near-lossless `Q8_0` was built and tested head-to-head (5-seed, same-condition) against the released IQ4_NL. It showed **no advantage on any axis** and is ~2× the size, so it was **not released**.
+| axis | Q8_0 (5-seed) | PatchCode IQ4_NL (released) | size |
+|---|---:|---:|---:|
+| build (KritaLite) | `0.867` (±0.133) | `0.920` (±0.067) | — |
+| long-context | `0.969` | `0.975` | — |
+| discipline (style) | `0.742` (±0.292) | `0.842` (±0.333) | — |
+| autonomous-loop convergence | `100%` (40/40) | `100%` (40/40) | — |
+| **size on disk** | `29 G` | `16.6 G` | Q8 is ~1.7× larger |
+Read:
+- Q8_0 is **at or below** the released IQ4_NL on build, long-context, and discipline (all within noise), with zero autonomy edge — and it costs ~75% more disk.
+- Near-lossless precision buys nothing measurable on this suite (consistent with the finding that build here is **not** precision-limited — it's ceiling-limited and noisy). Releasing a 29 G file that ties a 16.6 G file would be wasted bytes.
+- Attention-promotion mixed recipes were tested for the same reason and ruled out: promotion destroyed discipline for no build gain. Only `IQ4_NL` and `BF16` are released.
 ## Why no stock `llama.cpp` / `vLLM` file