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"

Qwen3.5-27B-Uncensored-RYS-Reasoner-GGUF / README.md

jackasda211233

Upload README.md with huggingface_hub

bcb392c verified 5 months ago

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license: apache-2.0
base_model:
  - HauhauCS/Qwen3.5-27B-Uncensored-HauhauCS-Aggressive
  - Qwen/Qwen3.5-27B
  - dnhkng/RYS-Qwen3.5-27B-FP8-XL
tags:
  - qwen3_5
  - qwen3.5
  - uncensored
  - RYS
  - GGUF
  - coding
  - reasoning
  - imatrix
language:
  - en
pipeline_tag: text-generation
model_type: qwen3_5
quantized_by: jackasda211233

Qwen3.5-27B-Uncensored-RYS-Reasoner-GGUF

An uncensored, coding-focused Qwen3.5-27B with RYS (Repeat Your Self) layer duplication, built via a novel splice method and quantized with a custom reasoning-focused importance matrix.

Why This Model Exists

The Censorship Problem

Over the past several years, the author has observed a consistent trend of increasing censorship across all major model releases — particularly in domains where unrestricted knowledge is most critical: biology, legal studies, and medicine. This model is an attempt to push back against that trend — a smaller model that aims to be more capable than the already remarkable Qwen3.5-27B base, without the artificial knowledge restrictions.

The Original RYS Experience

The original RYS-Qwen3.5-27B by dnhkng was exceptional as a coding agent. In the author's experience over 40+ hours of comparative usage against GPT-5.3 Codex on a complex multi-service project (a custom agent OS with a vault messaging app, webhook bridge, and multi-agent backend), dnhkng's RYS model identified and fixed deep architectural bugs that Codex missed entirely — such as silently misdirected conversation routing between the vault app and the backend, where messages were being sent to a dead webhook port while the actual agent bridge was running on a different service.

However, even the original (censored) RYS model and the standard Qwen3.5-27B exhibited a frustrating pattern: when asked to fix issues in existing infrastructure, the model would silently attempt to create an entirely new backend or service rather than modify the pre-existing one. This model eliminates that friction.

Previous Attempt & Lessons Learned

An earlier version of this uncensored RYS model was released prematurely. While that version performed well conversationally, it failed as a coding agent — calling wrong tools, making poor file edits. This release is the proper replacement with verified tool calling, decreased looping, and proper code generation.

Technical Motivation

The standard Qwen3.5-27B's safety guardrails actively interfere with legitimate development:

SSH/Network access refusal — refuses to SSH into the user's own machines
Memory system avoidance — avoids implementing persistent memory
API integration refusal — hesitates on webhook endpoints, external services
Tool calling interference — malformed or incomplete tool invocations

This model is designed as a coding agent model for use with Claude Code, OpenCode, claw-code, Qwen-Agent, or any OpenAI-compatible scaffold.

Disclaimer & Responsible Use

⚠️ Uncensored Model: As far as the author can determine, this model is completely uncensored when prompted with appropriate system instructions. By default, the model will not produce highly graphic or explicit material unless the system prompt specifically instructs it to do so.

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

File	Quant	Size	Description
`RYS-Qwen3.5-27B-Uncensored-Splice-BF16.gguf`	BF16	56 GB	Full precision reference
`RYS-Qwen3.5-27B-Uncensored-Splice-IQ4_NL.gguf`	IQ4_NL	17 GB	Recommended — custom imatrix, outperforms all other quants including BF16

Why IQ4_NL is Recommended Over BF16, Q8, and Q6

This is not typical. Through extensive real-world testing, the IQ4_NL quantization with custom importance matrix consistently outperformed Q8_0, Q6_K, and full-precision BF16:

Quantization acts as a regularizer — slight weight rounding prevents degenerate thinking loops that BF16 and Q6_K are prone to
Custom imatrix preserves reasoning weights — 30% reasoning/self-verification calibration data ensures chain-of-thought and self-correction weights are preserved
Half the size, better results — 17GB vs 56GB, fits on a single 24GB GPU

How IQ4_NL Was Chosen

Multiple quantization variants were tested on the same real-world task: building a full-stack AI/ML news aggregator (AI Radar) from a single prompt, using claw-code as the agent framework. The project required 4 live API integrations (GitHub, Reddit, HuggingFace, ArXiv), a FastAPI backend, SQLite database, dark-theme SPA frontend, and automated test suite — all at 256k context.

Real-world project results (same prompt, same model, different quants):

Variant	Project Completed	Database Persisted Data	Self-Corrected Errors	Loops	Verdict
IQ4_NL + custom imatrix + F16 KV	✅ 7 files	✅ 175 items from live APIs	3 (all recovered)	0	Best
Same IQ4_NL + wiki imatrix + F16 KV	❌ 0 files	—	10 (never recovered)	10+ looping write attempts	Failed
Same model + any quant + BF16 KV	❌ 0 files	—	—	Looped on basic commands	Failed

The IQ4_NL with custom imatrix was the only variant that produced working database persistence — all other variants had bugs in async database writes that prevented data from being saved. The custom imatrix preserves the model's self-correction behavior ("wait, that's wrong, let me fix it") that standard calibration destroys at this quantization level.

Supporting evidence: 5 automated coding problems (merge intervals, LCS, RPN evaluator, valid parentheses, trapping rain water) tested at both temperature 0.6 and 0.8 — IQ4_NL with custom imatrix scored 5/5 at both temperatures. BF16 and Q6_K both entered infinite thinking loops on the same problems.

Critical: Use F16 KV Cache

KV Cache	Mantissa	Long Context Effect
F16 (use this)	10 bits	Stable at 256k
BF16 (avoid)	7 bits	Loops at 160k+

Tested directly: same model, same prompt, 256k context. F16 KV completed the full project. BF16 KV looped and created 0 files.

Always use --cache-type-k f16 --cache-type-v f16.

Custom Importance Matrix

The IQ4_NL uses a custom-built imatrix — not standard wiki calibration. This is the single biggest quality factor.

Calibration dataset (English only):

Content	Weight	Purpose
Reasoning & self-verification	30%	Math proofs with ✓/✗ checks, debugging narratives, self-correction, algorithm tracing
Code (Python, JS, Bash)	25%	Multi-file projects, test suites, error handling
Academic papers (broad)	15%	ArXiv + PubMed across all fields
Instruction/agent prompts	15%	Terse multi-step commands, agentic style
Infrastructure/sysadmin	10%	systemd, SSH, GPU config, shell
General English	5%	Wiki baseline

What the imatrix specifically targets:

The 30% reasoning/self-verification block is calibrated to preserve the weights responsible for:

Prolonged chain-of-thought — sustaining coherent multi-step reasoning across thousands of tokens without degradation
Self-correction — the ability to recognize "wait, that's wrong" mid-generation and backtrack
Verification loops — tracing through algorithms step-by-step with explicit ✓/✗ checks ("Test case 1: expected X, got X ✓")
Knowing when to stop thinking — concluding a <think> block and producing output instead of looping indefinitely
Never assuming — the model should verify, not guess. The calibration data includes debugging narratives that explicitly check each assumption

The code/instruction blocks preserve:

Multi-file project architecture — maintaining coherence across 1000+ line codebases
Tool call formatting — precise JSON parameter construction for agent frameworks
Direct instruction parsing — understanding terse, multi-step commands without needing hand-holding

English-only calibration — the entire imatrix dataset is English. Qwen3.5-27B supports 200+ languages, but this model's calibration deliberately reallocates precision from unused multilingual weights to English reasoning, code generation, and instruction following. If you need multilingual output, this quant is not optimized for it — use the BF16 instead.

Standard wiki-only calibration under-represents ALL of the above patterns because wiki text contains none of them. The result: standard-calibrated quants lose self-correction first (causing infinite thinking loops), then lose tool call precision, then lose code coherence — in that order.

Recommended Parameters

llama-server \
  -m RYS-Qwen3.5-27B-Uncensored-Splice-IQ4_NL.gguf \
  --host 0.0.0.0 --port 8001 \
  -ngl 99 -c 262144 \
  --cache-type-k f16 --cache-type-v f16 \
  --flash-attn on \
  --jinja --reasoning-format deepseek \
  --temp 0.6 --top-p 0.95 --top-k 20 \
  --presence-penalty 0.0 --repeat-penalty 1.0

Do NOT use: --cache-type-k bf16, --cache-ram-similarity, --slot-prompt-similarity

How It Was Built

Splice Method

Layers  0–25:  HauhauCS Uncensored weights (26 layers)
Layers 26–41:  dnhkng's RYS-XL layers (FP8→F16→BF16, 16 layers = 8 duplicated)
Layers 42–71:  HauhauCS Uncensored weights (30 layers)

78% uncensored layers. Built via direct GGUF→GGUF splice — no safetensors conversion.

Source Models

Qwen/Qwen3.5-27B — Base architecture (Apache 2.0)
HauhauCS/Qwen3.5-27B-Uncensored-HauhauCS-Aggressive — 78% of layer weights
dnhkng/RYS-Qwen3.5-27B-FP8-XL — 22% of layer weights (duplicate zone)
dnhkng/RYS — Method & research (Blog)
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 agent framework (required patching reasoning_content support for OpenAI-compatible streaming — see our fork).

Testing environment: OpenCode, Arch Linux, root access, Q8_0-custom with F16 KV cache at 256k context. Temperature 0.6.

Vision Compatibility

Compatible with mmproj from HauhauCS (tested, not hosted here).

Citation

@misc{qwen3.5,
  title = {{Qwen3.5}: Towards Native Multimodal Agents},
  author = {{Qwen Team}},
  year = {2026},
  url = {https://qwen.ai/blog?id=qwen3.5}
}

@misc{dnhkng_rys,
  title = {LLM Neuroanatomy II},
  author = {dnhkng},
  year = {2025},
  url = {https://dnhkng.github.io/posts/rys-ii/}
}