Instructions to use vultr/Ling-3.0-flash-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 vultr/Ling-3.0-flash-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 vultr/Ling-3.0-flash-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf vultr/Ling-3.0-flash-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 vultr/Ling-3.0-flash-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf vultr/Ling-3.0-flash-GGUF:Q4_K_M

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 vultr/Ling-3.0-flash-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf vultr/Ling-3.0-flash-GGUF:Q4_K_M

Use Docker

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

Llama new
LM Studio
Jan

vLLM

How to use vultr/Ling-3.0-flash-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

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

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

How to use vultr/Ling-3.0-flash-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vultr/Ling-3.0-flash-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": "vultr/Ling-3.0-flash-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Hermes Agent

How to use vultr/Ling-3.0-flash-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 vultr/Ling-3.0-flash-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 vultr/Ling-3.0-flash-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use vultr/Ling-3.0-flash-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vultr/Ling-3.0-flash-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 "vultr/Ling-3.0-flash-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"

Ling 3.0 Flash, GGUF

Ling 3.0 Flash is a native hybrid-reasoning Mixture-of-Experts model, optimized for efficient long-context reasoning and production deployment. Designed for complex agentic workflows, it incorporates training for coding, general, and deep-research agents.

This repository holds Vultr's GGUF quantizations of it. They are hybrid quantizations: rather than applying one precision to the whole network, each layer's precision is chosen from a measured importance score, so the bits go where they change the output most.

Model Overview

Attribute	Value
Base model	inclusionAI/Ling-3.0-flash
Architecture	bailingmoe3, Mixture-of-Experts
Parameters	124B
Active parameters	5.1B
Layers	42
Hidden size	2,560
Attention heads	32
Experts	512 routed, 8 activated per token, 1 shared
Context length	up to 256K
Vocabulary	157,184
Attention layout	35 Kimi Delta Attention (KDA) layers with 7 gated MLA layers in a 5:1 pattern
Training environments	10,000+ interactive environments
License	MIT

Attribute	Value
Base model	inclusionAI/Ling-3.0-flash
Architecture	bailingmoe3, Mixture-of-Experts
Parameters	124B
Active parameters	5.1B
Layers	42
Hidden size	2,560
Attention heads	32
Experts	512 routed, 8 activated per token, 1 shared
Context length	up to 256K
Vocabulary	157,184
Attention layout	35 Kimi Delta Attention (KDA) layers with 7 gated MLA layers in a 5:1 pattern
Training environments	10,000+ interactive environments
License	MIT

Quantizations

File	Type	Size	KLD vs bf16
Ling-3.0-flash-Q5_K_M.gguf	Q5_K_M	97.16 GB	0.0085
Ling-3.0-flash-Q4_K_M.gguf	Q4_K_M	82.76 GB	0.0187
Ling-3.0-flash-IQ3_M.gguf	IQ3_M	59.74 GB	0.0543
Ling-3.0-flash-IQ3_S.gguf	IQ3_S	59.18 GB	0.0589

File	Type	Size	KLD vs bf16
Ling-3.0-flash-Q5_K_M.gguf	Q5_K_M	97.16 GB	0.0085
Ling-3.0-flash-Q4_K_M.gguf	Q4_K_M	82.76 GB	0.0187
Ling-3.0-flash-IQ3_M.gguf	IQ3_M	59.74 GB	0.0543
Ling-3.0-flash-IQ3_S.gguf	IQ3_S	59.18 GB	0.0589

KL divergence measures how far a quantized model's next-token probabilities drift from those of the original bf16 model, averaged over every token of the evaluation text. The lower the value, the closer the quantized model is to the original; zero would mean identical output.

Evaluation

Every variant in this repository was evaluated against the original bf16 model for perplexity, KL divergence, top-token agreement, HellaSwag and WinoGrande, under identical conditions. The full results are maintained on the model's page in the Vultr Model Library:

https://docs.vultr.com/models/inclusionai-ling-30/ling-3.0-flash?tab=quantized

That page is the reference for these files. The summary above is an excerpt from it.

Run

llama-server -hf vultr/Ling-3.0-flash-GGUF:Q4_K_M

The model's chat template is embedded in every file, so no extra prompt formatting is needed.

The repository also includes mtp-Ling-3.0-flash-Q8_0.gguf, the model's multi-token prediction (MTP) head, for optional speculative decoding with any quant above:

llama-server -hf vultr/Ling-3.0-flash-GGUF:Q4_K_M --spec-type draft-mtp

License

These files are derived from inclusionAI/Ling-3.0-flash and are distributed under the same license, MIT. The model is the work of inclusionAI; Vultr produced the quantizations.

Downloads last month: 348

GGUF

Model size

124B params

Architecture

bailingmoe3

Hardware compatibility

3-bit

4-bit

5-bit

8-bit

Model tree for vultr/Ling-3.0-flash-GGUF

Base model

inclusionAI/Ling-3.0-flash

Quantized

(49)

this model