Instructions to use cvgro/Qwen3.6-27B-Uncensored-Genesis-MTP-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 cvgro/Qwen3.6-27B-Uncensored-Genesis-MTP-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 cvgro/Qwen3.6-27B-Uncensored-Genesis-MTP-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf cvgro/Qwen3.6-27B-Uncensored-Genesis-MTP-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf cvgro/Qwen3.6-27B-Uncensored-Genesis-MTP-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf cvgro/Qwen3.6-27B-Uncensored-Genesis-MTP-GGUF:Q4_K_M

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 cvgro/Qwen3.6-27B-Uncensored-Genesis-MTP-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf cvgro/Qwen3.6-27B-Uncensored-Genesis-MTP-GGUF:Q4_K_M

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 cvgro/Qwen3.6-27B-Uncensored-Genesis-MTP-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf cvgro/Qwen3.6-27B-Uncensored-Genesis-MTP-GGUF:Q4_K_M

Use Docker

docker model run hf.co/cvgro/Qwen3.6-27B-Uncensored-Genesis-MTP-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use cvgro/Qwen3.6-27B-Uncensored-Genesis-MTP-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cvgro/Qwen3.6-27B-Uncensored-Genesis-MTP-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": "cvgro/Qwen3.6-27B-Uncensored-Genesis-MTP-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/cvgro/Qwen3.6-27B-Uncensored-Genesis-MTP-GGUF:Q4_K_M

Ollama
How to use cvgro/Qwen3.6-27B-Uncensored-Genesis-MTP-GGUF with Ollama:
```
ollama run hf.co/cvgro/Qwen3.6-27B-Uncensored-Genesis-MTP-GGUF:Q4_K_M
```
Unsloth Desktop

How to use cvgro/Qwen3.6-27B-Uncensored-Genesis-MTP-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf cvgro/Qwen3.6-27B-Uncensored-Genesis-MTP-GGUF:Q4_K_M

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": "cvgro/Qwen3.6-27B-Uncensored-Genesis-MTP-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use cvgro/Qwen3.6-27B-Uncensored-Genesis-MTP-GGUF with Docker Model Runner:
```
docker model run hf.co/cvgro/Qwen3.6-27B-Uncensored-Genesis-MTP-GGUF:Q4_K_M
```

Lemonade

How to use cvgro/Qwen3.6-27B-Uncensored-Genesis-MTP-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull cvgro/Qwen3.6-27B-Uncensored-Genesis-MTP-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.6-27B-Uncensored-Genesis-MTP-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use cvgro/Qwen3.6-27B-Uncensored-Genesis-MTP-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 cvgro/Qwen3.6-27B-Uncensored-Genesis-MTP-GGUF:Q4_K_M

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 cvgro/Qwen3.6-27B-Uncensored-Genesis-MTP-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use cvgro/Qwen3.6-27B-Uncensored-Genesis-MTP-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf cvgro/Qwen3.6-27B-Uncensored-Genesis-MTP-GGUF:Q4_K_M

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 "cvgro/Qwen3.6-27B-Uncensored-Genesis-MTP-GGUF:Q4_K_M" \
  --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"

cvgro

LuffyTheFox commited on Jul 29

Commit

f53b93f

0 Parent(s):

Duplicate from LuffyTheFox/Qwen3.6-27B-Uncensored-Genesis-MTP-GGUF

Browse files

Co-authored-by: Alexey Zakharchenko <LuffyTheFox@users.noreply.huggingface.co>

Files changed (12) hide show

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+---
+license: apache-2.0
+tags:
+- uncensored
+- qwen3.6
+- gguf
+- vision
+- multimodal
+- genesis
+language:
+- en
+- zh
+- multilingual
+pipeline_tag: image-text-to-text
+base_model:
+- HauhauCS/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive
+---
+> ⚡ [https://web.tribute.tg/d/KIH](https://web.tribute.tg/d/KIH) ⚡ If you like this Genesis LLM release you can [**donate**](https://web.tribute.tg/d/KIH) to me via [@Tribute](https://t.me/tribute) bot in Telegram messenger and support future Genesis LLM development.
+# 🌟 Qwen3.6-27B-Uncensored-HauhauCS-Aggressive -> Genesis
+> ⚡ **Why Genesis project exists?** During training, **ALL** models don't just learn knowledge - they also accumulate random noise in their tensors. This noise builds up and creates something I call the **Noise Gate** - a fundamental barrier that stops LLM models from learning further and makes them unstable, verbose, and prone to hallucinations. My approach removes this noise. It repairs the signal without touching the learned knowledge. The result is a model that finally speaks clearly, follows instructions, and remembers context - because it's no longer fighting its own internal chaos.
+> ⚡ **What is Genesis?** Genesis is post training data regeneration and calibrarion algorythm for neural networks (LLM) in GGUF format that I made with AI help during almost half a year of development. It's optimized, architecture independent, works with any model and based on mathematical statistics. I don't train or finetune models, I repair **purity of signal** in them instead on Google Collab Free on Tesla T4 GPU via Python based on how models learns information. On first stage I scan ssm_conv1d tensors in model, they handle long context memory. I repair balance in ssm_conv1d tensors via custom SVD. On second stage I scan model and detect noise in tensors via custom SVD. During scanning I exclude token_embd.weight, output.weight, ffn_gate_inp.weight, ffn_gate_inp_shexp.weight, 1D tensors, bias and norms. Then I reduce training noise in tensors via custom SVD with preserved training data, 99% of siginal and learned gradient. On third stage, I scan blocks in model via chunks via 3 parameters and pick best one that fits to weight distribution in tensor. Best picked chunk replaces zero chunks in broken tensor without touching learned structure in model
+Model is based on [HauhauCS/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive](https://huggingface.co/HauhauCS/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive) base.
+Thanks to [HauhauCS](https://huggingface.co/HauhauCS)
+> **[Join the Discord](https://discord.gg/SZ5vacTXYf)** for updates, roadmaps, projects, or just to chat.
+## Usage
+> This model is useful for uncensored local coding with LM Studio - Bionic agent.
+**Ready to use.** Recommended quant: **Q3_K_M (16GB) or Q4_K_M (24 GB)**
+**Recommended GPU VRAM**: not less than 16 GB of VRAM, 24 GB VRAM is recommended
+Tensor drift repair by me. Method: **Genesis**
+**Links:**
+- [Original uncensored model](https://huggingface.co/HauhauCS/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive)
+- [Quantization Script with Unsloth profiles support](https://pastebin.com/hXhcMJn9)
+---
+</details>
+LLM models often have:
+- **Saturated weights**: the model's activations are stuck, gradients vanish, outputs degrade.
+- **Scale mismatches**: one layer's weights are 10× larger than its peers for no good reason.
+- **Mean drift**: weight distributions shifted positive or negative, breaking symmetry assumptions.
+- **Zero blocks**: zero blocks corrupt the signal, turning training into noise amplification.
+- **Training Noise:** training noise increase randomness and ruins model output quality.
+My approach fixes all of that without retraining - pure numerical surgery on the raw bytes of the file.
+**Quantization script available here: https://pastebin.com/hXhcMJn9**
+Feel free to do your own quants if you want.
+## Any questions?
+Contact: luffythefox@mail.ru
+My Telegram: @LuffyTheFox
+## Recommended Settings for best perfomance
+Chat template: [chat_template.jinja](https://huggingface.co/froggeric/Qwen-Fixed-Chat-Templates/raw/main/chat_template.jinja)
+Set K Cache Quantization Type and V Cache Quantization Type to F16.
+Set GPU offload to maximum.
+For best model stability and first experience I recommend starting from this string in your System Prompt with enabled thinking and nothing else:
+`You are Qwen, a large language model created by Tongyi Lab team from Alibaba Group. You are a helpful assistant.`
+or this string
+`You are a helpful assistant.`
+If you want to bring more creativity to model use this System Prompt: [System_Prompt_Creative.txt](https://huggingface.co/LuffyTheFox/Qwen3.6-35B-A3B-Uncensored-Genesis-Hermes-V5-GGUF/raw/main/System_Prompt_Creative.txt)
+**Thinking mode (coding):**
+- Coding/precise tasks: `temperature=0.6, top_p=0.95, top_k=20, min_p=0, seed=42, presence_penalty=disabled, repeat_penalty=disabled`
+- General: `temperature=1.0, top_p=0.95, top_k=20, min_p=0.05, seed=42, presence_penalty=disabled, repeat_penalty=disabled`
+**Non Thinking mode (creative):**
+- General: `temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=42, presence_penalty=disabled, repeat_penalty=disabled`
+**Important:**
+- Keep at least 128K context to preserve thinking capabilities
+- Use `--jinja` flag with llama.cpp for proper chat template handling
+- Vision support requires the `mmproj` file alongside the main GGUF
+---
+## Specs
+- 27B dense parameters
+- 64 layers, layout: `16 × (3 × (Gated DeltaNet → FFN) → 1 × (Gated Attention → FFN))`
+- 48 linear attention layers + 16 full gated-attention layers
+- Gated DeltaNet: 48 V heads / 16 QK heads, head dim 128
+- Gated Attention: 24 Q heads / 4 KV heads, head dim 256, rope dim 64
+- Hidden dim 5120, FFN dim 17408, vocab 248320
+- 262K native context, extensible to ~1M with YaRN
+- Natively multimodal (text, image, video) — ships with mmproj
+- Based on [HauhauCS/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive](https://huggingface.co/HauhauCS/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive)
+---
+## Compatibility
+Works with llama.cpp, LM Studio, koboldcpp, and other GGUF-compatible runtimes.

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