Instructions to use jackasda211233/Qwen3.5-27B-Uncensored-RYS-Reasoner-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/Qwen3.5-27B-Uncensored-RYS-Reasoner-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/Qwen3.5-27B-Uncensored-RYS-Reasoner-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf jackasda211233/Qwen3.5-27B-Uncensored-RYS-Reasoner-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jackasda211233/Qwen3.5-27B-Uncensored-RYS-Reasoner-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf jackasda211233/Qwen3.5-27B-Uncensored-RYS-Reasoner-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/Qwen3.5-27B-Uncensored-RYS-Reasoner-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf jackasda211233/Qwen3.5-27B-Uncensored-RYS-Reasoner-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/Qwen3.5-27B-Uncensored-RYS-Reasoner-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jackasda211233/Qwen3.5-27B-Uncensored-RYS-Reasoner-GGUF:BF16

Use Docker

docker model run hf.co/jackasda211233/Qwen3.5-27B-Uncensored-RYS-Reasoner-GGUF:BF16

LM Studio
Jan

vLLM

How to use jackasda211233/Qwen3.5-27B-Uncensored-RYS-Reasoner-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jackasda211233/Qwen3.5-27B-Uncensored-RYS-Reasoner-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jackasda211233/Qwen3.5-27B-Uncensored-RYS-Reasoner-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jackasda211233/Qwen3.5-27B-Uncensored-RYS-Reasoner-GGUF:BF16

Ollama
How to use jackasda211233/Qwen3.5-27B-Uncensored-RYS-Reasoner-GGUF with Ollama:
```
ollama run hf.co/jackasda211233/Qwen3.5-27B-Uncensored-RYS-Reasoner-GGUF:BF16
```
Unsloth Desktop

How to use jackasda211233/Qwen3.5-27B-Uncensored-RYS-Reasoner-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/Qwen3.5-27B-Uncensored-RYS-Reasoner-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/Qwen3.5-27B-Uncensored-RYS-Reasoner-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use jackasda211233/Qwen3.5-27B-Uncensored-RYS-Reasoner-GGUF with Docker Model Runner:
```
docker model run hf.co/jackasda211233/Qwen3.5-27B-Uncensored-RYS-Reasoner-GGUF:BF16
```

Lemonade

How to use jackasda211233/Qwen3.5-27B-Uncensored-RYS-Reasoner-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jackasda211233/Qwen3.5-27B-Uncensored-RYS-Reasoner-GGUF:BF16

Run and chat with the model

lemonade run user.Qwen3.5-27B-Uncensored-RYS-Reasoner-GGUF-BF16

List all available models

lemonade list

Hermes Agent

How to use jackasda211233/Qwen3.5-27B-Uncensored-RYS-Reasoner-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/Qwen3.5-27B-Uncensored-RYS-Reasoner-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/Qwen3.5-27B-Uncensored-RYS-Reasoner-GGUF:BF16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use jackasda211233/Qwen3.5-27B-Uncensored-RYS-Reasoner-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/Qwen3.5-27B-Uncensored-RYS-Reasoner-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/Qwen3.5-27B-Uncensored-RYS-Reasoner-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 Apr 12

Commit

7c37a98

verified ·

1 Parent(s): 9319c37

Trim repeated README sections and keep top-level summary

Browse files

Files changed (1) hide show

README.md +13 -53

README.md CHANGED Viewed

@@ -5,7 +5,6 @@ base_model:
   - Qwen/Qwen3.5-27B
   - dnhkng/RYS-Qwen3.5-27B-FP8-XL
 tags:
-  - qwen3_5
   - qwen3.5
   - uncensored
   - RYS
@@ -56,16 +55,20 @@ This model is designed as a **coding agent model** for use with Claude Code, Ope
 >
 > **The author is not responsible for how this model is used.** Any actions taken are solely the responsibility of the user. Use in accordance with applicable laws and ethical standards.
-## Model Overview
-| Property | Value |
-|----------|-------|
-| **Architecture** | Qwen3.5 (Hybrid SSM — Gated DeltaNet + Full Attention) |
-| **Total Layers** | 72 (64 base + 8 duplicated via RYS) |
-| **Parameters** | ~29.8B |
-| **RYS Config** | (26,34) — dnhkng's validated Pareto-optimal XL configuration |
-| **Context Length** | 262,144 tokens |
-| **Thinking Mode** | Yes (DeepSeek-style `<think>...</think>`) |
 ## Available Files
@@ -215,14 +218,6 @@ llama-server \
   --min-p 0.0 --repeat-penalty 1.0
 ```
-**Current params:**
-- temp `0.7`
-- top-p `0.95`
-- top-k `20`
-- min-p `0.0`
-- repeat-penalty `1.0`
-- cache-ram `30720`
 For contexts above ~160k, consider switching the KV cache to F32:
 - `--cache-type-k f32 --cache-type-v f32`
@@ -254,20 +249,6 @@ Layers 42–71:  HauhauCS Uncensored weights (30 layers)
 - **[dnhkng/RYS](https://github.com/dnhkng/RYS)** — Method & research ([Blog](https://dnhkng.github.io/posts/rys-ii/))
 - **[ikawrakow/ik_llama.cpp](https://github.com/ikawrakow/ik_llama.cpp)** — Quantization tooling
-## Live Test: AI Radar Project (One-Shot)
-The model autonomously built a complete AI/ML news aggregator:
-- **26KB FastAPI backend** with 4 live API integrations (GitHub, Reddit, HuggingFace, ArXiv)
-- **25KB dark-theme SPA frontend** (881 lines, search, filters, cards, bookmarks)
-- **SQLite database** with 175 items persisted from live API fetches
-- **Setup & test scripts** — venv, deps, 9 endpoint tests
-- **Self-corrected** 3 tool format errors autonomously
-- **Zero loops** across ~70k token generation at 256k context
-Tested via [claw-code](https://github.com/ultraworkers/claw-code) agent framework (required patching `reasoning_content` support for OpenAI-compatible streaming — see our fork).
-Testing environment: [OpenCode](https://github.com/opencode-ai/opencode) on Arch Linux with root access. Primary runtime testing used ik-llama.cpp with the build info below and the current recommended parameters from this README.
 ## Tested Runtimes / Builds
 Primary validation for this release used **ik-llama.cpp**, mainly because its **graph split** is a major performance boost on multi-GPU systems compared to standard layer split.
@@ -299,27 +280,6 @@ The new `RYS-Qwen3.5-27B-Uncensored-Splice-IQ4_NL-llama.cpp-compatible.gguf` was
 | CUDA | ON |
 | Flash Attention | ON |
-Quantization command used:
-```bash
-llama-quantize \
-  --imatrix /home/benbi/Desktop/RYS-uncensored-experiment/splice_custom.imatrix \
-  /home/benbi/qwen35_uncensored_rys/Qwen3.5-27B-Uncensored-RYS-Splice-BF16.gguf \
-  /home/benbi/qwen35_uncensored_rys/Qwen3.5-27B-Uncensored-RYS-Splice-IQ4_NL-mainline.gguf \
-  IQ4_NL
-```
-Compatibility validation used:
-- `-c 262144`
-- `--cache-type-k f32 --cache-type-v f32`
-Verification prompts returned correct final outputs:
-- `0.05`
-- `5050`
-- `RYS 3-2-2 FP32 verification passed.`
 In either runtime, **use the latest available compile/build when testing**.
 ## Vision Compatibility

   - Qwen/Qwen3.5-27B
   - dnhkng/RYS-Qwen3.5-27B-FP8-XL
 tags:
   - qwen3.5
   - uncensored
   - RYS
 >
 > **The author is not responsible for how this model is used.** Any actions taken are solely the responsibility of the user. Use in accordance with applicable laws and ethical standards.
+## Representative Live Test
+The model autonomously built a complete AI/ML news aggregator:
+- **26KB FastAPI backend** with 4 live API integrations (GitHub, Reddit, HuggingFace, ArXiv)
+- **25KB dark-theme SPA frontend** (881 lines, search, filters, cards, bookmarks)
+- **SQLite database** with 175 items persisted from live API fetches
+- **Setup & test scripts** — venv, deps, 9 endpoint tests
+- **Self-corrected** 3 tool format errors autonomously
+- **Zero loops** across ~70k token generation at 256k context
+Tested via [claw-code](https://github.com/ultraworkers/claw-code) agent framework (required patching `reasoning_content` support for OpenAI-compatible streaming — see our fork).
+Testing environment: [OpenCode](https://github.com/opencode-ai/opencode) on Arch Linux with root access. Primary runtime testing used ik-llama.cpp with the build info below and the current recommended parameters from this README.
 ## Available Files
   --min-p 0.0 --repeat-penalty 1.0
 ```
 For contexts above ~160k, consider switching the KV cache to F32:
 - `--cache-type-k f32 --cache-type-v f32`
 - **[dnhkng/RYS](https://github.com/dnhkng/RYS)** — Method & research ([Blog](https://dnhkng.github.io/posts/rys-ii/))
 - **[ikawrakow/ik_llama.cpp](https://github.com/ikawrakow/ik_llama.cpp)** — Quantization tooling
 ## Tested Runtimes / Builds
 Primary validation for this release used **ik-llama.cpp**, mainly because its **graph split** is a major performance boost on multi-GPU systems compared to standard layer split.
 | CUDA | ON |
 | Flash Attention | ON |
 In either runtime, **use the latest available compile/build when testing**.
 ## Vision Compatibility