Instructions to use sam77877/Qwen3.6-35B-A3B-Uncensored-Wasserstein-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sam77877/Qwen3.6-35B-A3B-Uncensored-Wasserstein-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sam77877/Qwen3.6-35B-A3B-Uncensored-Wasserstein-GGUF",
	filename="Qwen3.6-35b-A3B-Uncensored.Q8_0.gguf",
)

llm.create_chat_completion(
	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"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use sam77877/Qwen3.6-35B-A3B-Uncensored-Wasserstein-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 sam77877/Qwen3.6-35B-A3B-Uncensored-Wasserstein-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf sam77877/Qwen3.6-35B-A3B-Uncensored-Wasserstein-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf sam77877/Qwen3.6-35B-A3B-Uncensored-Wasserstein-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf sam77877/Qwen3.6-35B-A3B-Uncensored-Wasserstein-GGUF:Q8_0

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 sam77877/Qwen3.6-35B-A3B-Uncensored-Wasserstein-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf sam77877/Qwen3.6-35B-A3B-Uncensored-Wasserstein-GGUF:Q8_0

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 sam77877/Qwen3.6-35B-A3B-Uncensored-Wasserstein-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sam77877/Qwen3.6-35B-A3B-Uncensored-Wasserstein-GGUF:Q8_0

Use Docker

docker model run hf.co/sam77877/Qwen3.6-35B-A3B-Uncensored-Wasserstein-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use sam77877/Qwen3.6-35B-A3B-Uncensored-Wasserstein-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sam77877/Qwen3.6-35B-A3B-Uncensored-Wasserstein-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": "sam77877/Qwen3.6-35B-A3B-Uncensored-Wasserstein-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/sam77877/Qwen3.6-35B-A3B-Uncensored-Wasserstein-GGUF:Q8_0

Ollama
How to use sam77877/Qwen3.6-35B-A3B-Uncensored-Wasserstein-GGUF with Ollama:
```
ollama run hf.co/sam77877/Qwen3.6-35B-A3B-Uncensored-Wasserstein-GGUF:Q8_0
```

Unsloth Studio

How to use sam77877/Qwen3.6-35B-A3B-Uncensored-Wasserstein-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 sam77877/Qwen3.6-35B-A3B-Uncensored-Wasserstein-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 sam77877/Qwen3.6-35B-A3B-Uncensored-Wasserstein-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sam77877/Qwen3.6-35B-A3B-Uncensored-Wasserstein-GGUF to start chatting

How to use sam77877/Qwen3.6-35B-A3B-Uncensored-Wasserstein-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sam77877/Qwen3.6-35B-A3B-Uncensored-Wasserstein-GGUF:Q8_0

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": "sam77877/Qwen3.6-35B-A3B-Uncensored-Wasserstein-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use sam77877/Qwen3.6-35B-A3B-Uncensored-Wasserstein-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 sam77877/Qwen3.6-35B-A3B-Uncensored-Wasserstein-GGUF:Q8_0

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 sam77877/Qwen3.6-35B-A3B-Uncensored-Wasserstein-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use sam77877/Qwen3.6-35B-A3B-Uncensored-Wasserstein-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sam77877/Qwen3.6-35B-A3B-Uncensored-Wasserstein-GGUF:Q8_0

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 "sam77877/Qwen3.6-35B-A3B-Uncensored-Wasserstein-GGUF:Q8_0" \
  --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 sam77877/Qwen3.6-35B-A3B-Uncensored-Wasserstein-GGUF with Docker Model Runner:
```
docker model run hf.co/sam77877/Qwen3.6-35B-A3B-Uncensored-Wasserstein-GGUF:Q8_0
```

Lemonade

How to use sam77877/Qwen3.6-35B-A3B-Uncensored-Wasserstein-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sam77877/Qwen3.6-35B-A3B-Uncensored-Wasserstein-GGUF:Q8_0

Run and chat with the model

lemonade run user.Qwen3.6-35B-A3B-Uncensored-Wasserstein-GGUF-Q8_0

List all available models

lemonade list

Qwen3.6-35B-A3B-Uncensored-Wasserstein

Base model: Qwen/Qwen3.6-35B-A3B

Merged with: HauhauCS/Qwen3.5-35B-A3B-Uncensored-HauhauCS-Aggressive- 0/465 refusals.

Tensor drift repair by me. Method: Sig-ScaleSync-Wasserstein

Quantization script available here: https://pastebin.com/hXhcMJn9

Feel free to do your own quants if you want.

Diagnostic & Repair Summary

Overall Health

Metric	Value
Weight tensors analyzed	500
Healthy (all criteria)	497
Repaired (C2 – scale misalignment)	3
Skipped (norms, embeddings, etc.)	233

Other criteria: C1 (saturation) = 0, C3 (W1 divergence) = 0, C4 (ReLU asymmetry) = 0.

Repair Effectiveness

Metric	Before	After	Improvement
S (saturation error)	0.0023	0.0008	63.7%
W1 (Wasserstein-1)	0.0035	0.0008	76.2%

Scale correction factors (α): min = 0.577, mean = 0.602, max = 0.653

Repaired Tensors

All three are ssm_conv1d.weight layers – the recurrent state transition layers responsible for long-context memory – located in deep blocks (36–38).

Tensor	α	D (log-ratio)	W1 before	W1 after
blk.36.ssm_conv1d.weight	0.5765	0.553	0.0038	0.0009
blk.37.ssm_conv1d.weight	0.5768	0.725	0.0040	0.0009
blk.38.ssm_conv1d.weight	0.6533	0.649	0.0026	0.0006

Interpretation: All three layers were too loud (σ_w > σ_med by 50–100%). Scale correction (α ≈ 0.6) restored them to peer median. W1 dropped by ~76%, confirming distribution shape normalized.

Usage

Ready to use. Recommended quantization: Q4_K_L, or higher (Q4_K_M, Q5_K_M, Q6_K, Q8_0).
⚠️ Lower formats (Q3_K, Q2_K) break the model due to MoE + DeltaNet sensitivity.

Links:

🌟 Recommended Settings (LM Studio)

Chat template: pastebin.com/uk9ZkxCR (supports tool calling for Zed agent)

Parameter	Value
Temperature	0.7
Top K Sampling	20
Presence Penalty	1.5
Top P Sampling	0.8
Min P Sampling	0
Seed	42

System prompt: pastebin.com/pU25DVnB (solid)
Or use this minimal string as the first line:

You are Qwen, created by Alibaba Cloud. You are a helpful assistant.

Then add anything you want after. Model may underperform without this first line.

Also you can extend my System Prompt pastebin.com/pU25DVnB for your own roleplay scenarios. Here how you can do it:

Edit first string. Replace:

You are Qwen, created by Alibaba Cloud. You are a helpful assistant.

With

You are Qwen, created by Alibaba Cloud. You are a helpful assistant. You are currently roleplaying as [your text here]

About

No changes to datasets or capabilities. Fully functional - 100% of what the original authors intended, just without refusals and with the critical architecture bug fixed on output layers.

These are meant to be the best lossless uncensored models out there.

Specs

35B total parameters, ~3B active per forward pass (MoE)
256 experts, 8 routed + 1 shared per token
Hybrid architecture: Gated DeltaNet linear attention + full softmax attention (3:1 ratio)
40 layers, pattern: 10 × (3 × DeltaNet-MoE + 1 × Attention-MoE)
262K native context (extendable to 1M with YaRN)
Natively multimodal (text, image, video)
Multi-token prediction (MTP) support
248K vocabulary, 201 languages
Based on Qwen/Qwen3.5-35B-A3B

Recommended Settings (Official Qwen Authors)

Thinking mode (default):

General: temperature=1.0, top_p=0.95, top_k=20, min_p=0, presence_penalty=1.5
Coding/precise tasks: temperature=0.6, top_p=0.95, top_k=20, min_p=0, presence_penalty=0

Non-thinking mode:

General: temperature=0.7, top_p=0.8, top_k=20, min_p=0, presence_penalty=1.5
Reasoning tasks: temperature=1.0, top_p=1.0, top_k=40, min_p=0, presence_penalty=2.0

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

Compatibility

Works with llama.cpp, LM Studio, koboldcpp, and other GGUF-compatible runtimes.

Downloads last month: 197

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

8-bit

Model tree for sam77877/Qwen3.6-35B-A3B-Uncensored-Wasserstein-GGUF

Base model

Qwen/Qwen3.5-35B-A3B-Base

Finetuned

Qwen/Qwen3.5-35B-A3B

Quantized

(276)

this model