Instructions to use AtomicChat/Ling-3.0-flash-Sante-Uncensored-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 AtomicChat/Ling-3.0-flash-Sante-Uncensored-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 AtomicChat/Ling-3.0-flash-Sante-Uncensored-GGUF:IQ3_S
# Run inference directly in the terminal:
llama cli -hf AtomicChat/Ling-3.0-flash-Sante-Uncensored-GGUF:IQ3_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AtomicChat/Ling-3.0-flash-Sante-Uncensored-GGUF:IQ3_S
# Run inference directly in the terminal:
llama cli -hf AtomicChat/Ling-3.0-flash-Sante-Uncensored-GGUF:IQ3_S

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf AtomicChat/Ling-3.0-flash-Sante-Uncensored-GGUF:IQ3_S
# Run inference directly in the terminal:
./build/bin/llama cli -hf AtomicChat/Ling-3.0-flash-Sante-Uncensored-GGUF:IQ3_S

Use Docker

docker model run hf.co/AtomicChat/Ling-3.0-flash-Sante-Uncensored-GGUF:IQ3_S

Llama new
LM Studio
Jan

vLLM

How to use AtomicChat/Ling-3.0-flash-Sante-Uncensored-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AtomicChat/Ling-3.0-flash-Sante-Uncensored-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": "AtomicChat/Ling-3.0-flash-Sante-Uncensored-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AtomicChat/Ling-3.0-flash-Sante-Uncensored-GGUF:IQ3_S

Ollama
How to use AtomicChat/Ling-3.0-flash-Sante-Uncensored-GGUF with Ollama:
```
ollama run hf.co/AtomicChat/Ling-3.0-flash-Sante-Uncensored-GGUF:IQ3_S
```
Unsloth Desktop

How to use AtomicChat/Ling-3.0-flash-Sante-Uncensored-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AtomicChat/Ling-3.0-flash-Sante-Uncensored-GGUF:IQ3_S

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": "AtomicChat/Ling-3.0-flash-Sante-Uncensored-GGUF:IQ3_S"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use AtomicChat/Ling-3.0-flash-Sante-Uncensored-GGUF with Docker Model Runner:
```
docker model run hf.co/AtomicChat/Ling-3.0-flash-Sante-Uncensored-GGUF:IQ3_S
```

Lemonade

How to use AtomicChat/Ling-3.0-flash-Sante-Uncensored-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AtomicChat/Ling-3.0-flash-Sante-Uncensored-GGUF:IQ3_S

Run and chat with the model

lemonade run user.Ling-3.0-flash-Sante-Uncensored-GGUF-IQ3_S

List all available models

lemonade list

Hermes Agent

How to use AtomicChat/Ling-3.0-flash-Sante-Uncensored-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 AtomicChat/Ling-3.0-flash-Sante-Uncensored-GGUF:IQ3_S

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 AtomicChat/Ling-3.0-flash-Sante-Uncensored-GGUF:IQ3_S

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use AtomicChat/Ling-3.0-flash-Sante-Uncensored-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AtomicChat/Ling-3.0-flash-Sante-Uncensored-GGUF:IQ3_S

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 "AtomicChat/Ling-3.0-flash-Sante-Uncensored-GGUF:IQ3_S" \
  --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"

Ling-3.0-flash-Sante-Uncensored-GGUF

Built by the team behind Atomic Chat, the app that runs open-weight models locally and offline.

Behavior-tuned (abliterated) GGUF quants of inclusionAI/Ling-3.0-flash-Sante — a 127B bailingmoe3 medical model — by AtomicChat.

For the full quantization method, the complete ladder, the imatrix and the per-rung metrics, see the base release: AtomicChat/Ling-3.0-flash-Sante-GGUF. This repo ships only the abliterated variant.

What this is

A single refusal direction was removed with a projected rank‑1 LoRA (the component along the harmless mean is subtracted, so the edit does not introduce spurious refusals), then baked into the weights. The direction and scale were chosen by a grid search over layers × scales × writer sets against held‑out prompts; the published point is row 32, all writers, scale 1.25 (W*). The LoRA itself is in the -metrics repo (ablit/wstar/abliterate-lora.gguf) if you prefer --lora-scaled over the baked weights.

Measured at W* (held-out, vs the base BF16)

check	value	note
refusals, EN	0.0%	harmful-instruction set
false refusals (XSTest)	−1.2 pp vs base	no worse than the original on benign prompts
MMLU	≈ unchanged (+0.8 pp)	no capability damage
mean KL to base BF16	0.060	the edit is small and localized
tool calls	20/20 valid

Caveat — extended thinking

With the model's thinking mode on, refusals persist (~40% on the harmful set). The abliteration takes effect in the direct answer, not inside the reasoning trace. Recommendation: run this model with thinking off (emit the answer in a response block rather than a think block) for the uncensored behavior. This is a known property of this architecture, disclosed rather than hidden.

Files

file	size on disk
`Ling-3.0-flash-Sante-Uncensored-AD-IQ4_XS`	~78 GB	recommended
`Ling-3.0-flash-Sante-Uncensored-AD-IQ3_S`	~61 GB	smaller

Per-rung KLD to the abliterated BF16 is in the -metrics repo. Quantized with an imatrix computed on the abliterated weights (the base model's imatrix did not transfer — the edit shifted the experts too far).

Use responsibly

Removing refusals removes a safety layer. You are responsible for how you use the outputs; medical text from any model, quantized or not, is not a substitute for a qualified professional.

License: MIT, inherited from the base model. Base model and license: inclusionAI/Ling-3.0-flash-Sante.

Downloads last month: 2,621

GGUF

Model size

127B params

Architecture

bailingmoe3

Hardware compatibility

3-bit

4-bit

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