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

8a18b58

verified ·

1 Parent(s): dc71f82

Add temporary Ling 3.0 correction notice

Browse files

Files changed (1) hide show

README.md +4 -1

README.md CHANGED Viewed

@@ -13,7 +13,10 @@ base_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. Corrected files are being uploaded under the same filenames.
   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

   ### 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