Instructions to use bloomer010/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 bloomer010/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 bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL

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 bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL

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 bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL

Use Docker

docker model run hf.co/bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL

LM Studio
Jan

vLLM

How to use bloomer010/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 "bloomer010/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": "bloomer010/Ling-3.0-flash-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL

Ollama
How to use bloomer010/Ling-3.0-flash-GGUF with Ollama:
```
ollama run hf.co/bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL
```
Unsloth Desktop

How to use bloomer010/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 bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL

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": "bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use bloomer010/Ling-3.0-flash-GGUF with Docker Model Runner:
```
docker model run hf.co/bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL
```

Lemonade

How to use bloomer010/Ling-3.0-flash-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL

Run and chat with the model

lemonade run user.Ling-3.0-flash-GGUF-UD-Q4_K_XL

List all available models

lemonade list

Hermes Agent

How to use bloomer010/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 bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL

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 bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use bloomer010/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 bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL

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 "bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL" \
  --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"

bloomer010 commited on Aug 6

Commit

c0edc4f

verified ·

1 Parent(s): 8a18b58

Shorten correction notice

Browse files

Files changed (1) hide show

README.md +5 -12

README.md CHANGED Viewed

@@ -10,20 +10,13 @@ base_model:
   Stock llama.cpp builds without bailingmoe3 support will not load the model.
-  ### SwiGLU metadata correction in progress
-  The original GGUF revisions omitted the trained per-layer SwiGLU clamp metadata declared by the released model configuration. This
-  can cause rare but severe output corruption in later layers.
-  The repository correction is not yet complete. A tokenizer and tool-template audit is in progress so every required metadata change
-  can be included in one replacement pass. Please wait for the completion notice before downloading these files again.
-  Files downloaded before this correction should be downloaded again after completion, or repaired locally with
-  [`add-ling3-clamp-metadata.py`](./add-ling3-clamp-metadata.py). The repair changes GGUF metadata only. It does not alter or requantize
-  tensor data.
-  The corrected llama.cpp converter and loader are available in the linked fork. Commit:
-  [`c51308d8`](https://github.com/aetherbird/llama.cpp/commit/c51308d8)
   ## Conversion and Quantization

   Stock llama.cpp builds without bailingmoe3 support will not load the model.
+  ### ⚠️ Correction in progress
+  Earlier GGUF revisions omitted Ling 3.0's trained SwiGLU clamp metadata. Please wait for the completion notice before downloading;
+  a combined metadata and template audit is underway.
+  Existing files will only need redownloading or [local repair](./add-ling3-clamp-metadata.py), not requantization. llama.cpp fix:
+  [`c51308d8`](https://github.com/aetherbird/llama.cpp/commit/c51308d8).
   ## Conversion and Quantization