Instructions to use AtomicChat/Ling-3.0-flash-VL-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-VL-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-VL-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AtomicChat/Ling-3.0-flash-VL-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 AtomicChat/Ling-3.0-flash-VL-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AtomicChat/Ling-3.0-flash-VL-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 AtomicChat/Ling-3.0-flash-VL-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf AtomicChat/Ling-3.0-flash-VL-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 AtomicChat/Ling-3.0-flash-VL-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AtomicChat/Ling-3.0-flash-VL-GGUF:Q4_K_M

Use Docker

docker model run hf.co/AtomicChat/Ling-3.0-flash-VL-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use AtomicChat/Ling-3.0-flash-VL-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-VL-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-VL-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/AtomicChat/Ling-3.0-flash-VL-GGUF:Q4_K_M

Ollama
How to use AtomicChat/Ling-3.0-flash-VL-GGUF with Ollama:
```
ollama run hf.co/AtomicChat/Ling-3.0-flash-VL-GGUF:Q4_K_M
```
Unsloth Desktop

How to use AtomicChat/Ling-3.0-flash-VL-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-VL-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": "AtomicChat/Ling-3.0-flash-VL-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use AtomicChat/Ling-3.0-flash-VL-GGUF with Docker Model Runner:
```
docker model run hf.co/AtomicChat/Ling-3.0-flash-VL-GGUF:Q4_K_M
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AtomicChat/Ling-3.0-flash-VL-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Ling-3.0-flash-VL-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use AtomicChat/Ling-3.0-flash-VL-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-VL-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 AtomicChat/Ling-3.0-flash-VL-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use AtomicChat/Ling-3.0-flash-VL-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-VL-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 "AtomicChat/Ling-3.0-flash-VL-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"

How to Run Ling 3.0 Flash VL Locally

Built from InclusionAI's original weights using Atomic Chat's existing Ling importance matrix. The calibration corpora behind our builds are public.

Ling 3.0 Flash VL is InclusionAI's vision-language model for text and image inputs.
Choose AD-Q4_K_M, AD-Q5_K_M, AD-Q6_K, or AD-Q8_0. Image inputs use the shared F32 vision projector.
These builds are an experimental preview. Use the accompanying runtime; full quality validation is still in progress.

Prepared from the original inclusionAI checkpoint, revision 869591498e8dbb41d4d96e3e2a5b428a2f70eb1e. These are the Atomic AD layouts, made on CPU using the existing calibration iMatrix. The full nine-variant text comparison and CPU speed measurements are complete; see FINAL_REPORT.md. This repository is an experimental working preview, not a completed benchmark release.

Uploads arrive progressively. Check UPLOAD_STATUS.json before downloading a variant. Each language variant requires all six GGUF shards in the same directory; select shard 00001 when loading.

Variant	Complete language files, decimal GB
AD-Q4_K_M	79.30
AD-Q5_K_M	89.44
AD-Q6_K	107.14
AD-Q8_0	132.73

Vision requires the shared F32 mmproj, an additional 1.74 GB. File size is not a RAM requirement estimate.

Runtime requirement

Use the accompanying private runtime patch and added source files, based on AtomicBot-ai/atomic-llama-cpp-turboquant@cd560939087c95b93a1f30a95603d6b079436952. Stock llama.cpp and the released Atomic Chat app have not been validated for these artifacts. See RUNTIME.md.

Checks completed

All four variants: 917 tensor types/shapes verified, six-shard integrity checked, complete SHA-256 manifests, and all 382 protected F32 tensor payloads unchanged from BF16. Text and a spatial image passed on all four. Q4/Q6/Q8 additionally passed the OCR and object-count smoke cases. These are functional smoke checks, not a comprehensive vision benchmark.

Full text quality comparisons use the historical held-out 92 × 4096 protocol and a fresh BF16 reference from this checkpoint. Full text results are available in FINAL_REPORT.md. Video and maximum context are not validated.

The existing iMatrix comes from AtomicChat/Ling-3.0-flash-GGUF@253738fe190c15f329001f263f355fc1562bbe7c, SHA-256 7d3c0ebe9eb235cc08e0b7c91886f5c422772ea95eebbf0f53b0974a1c040991. Its 573 entries match the new language tensor dimensions. Four routed experts have no observations in that matrix; uniform importance was used for those entries.

manifest.json and SHA256SUMS describe the complete intended set. Actual upload completion is recorded separately in UPLOAD_STATUS.json.

Completed measurements

Full report · KLD chart · Metrics CSV.

The pilot results are separate from the full 92-block comparison. Completion is not a comprehensive vision or agentic quality certification.

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GGUF

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124B params

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bailingmoe3

Hardware compatibility

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