Instructions to use EryriLabs/Lightning-Herald-30B-A3B-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 EryriLabs/Lightning-Herald-30B-A3B-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 EryriLabs/Lightning-Herald-30B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf EryriLabs/Lightning-Herald-30B-A3B-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 EryriLabs/Lightning-Herald-30B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf EryriLabs/Lightning-Herald-30B-A3B-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 EryriLabs/Lightning-Herald-30B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf EryriLabs/Lightning-Herald-30B-A3B-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 EryriLabs/Lightning-Herald-30B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf EryriLabs/Lightning-Herald-30B-A3B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/EryriLabs/Lightning-Herald-30B-A3B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use EryriLabs/Lightning-Herald-30B-A3B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EryriLabs/Lightning-Herald-30B-A3B-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": "EryriLabs/Lightning-Herald-30B-A3B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/EryriLabs/Lightning-Herald-30B-A3B-GGUF:Q4_K_M

Ollama
How to use EryriLabs/Lightning-Herald-30B-A3B-GGUF with Ollama:
```
ollama run hf.co/EryriLabs/Lightning-Herald-30B-A3B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use EryriLabs/Lightning-Herald-30B-A3B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf EryriLabs/Lightning-Herald-30B-A3B-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": "EryriLabs/Lightning-Herald-30B-A3B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use EryriLabs/Lightning-Herald-30B-A3B-GGUF with Docker Model Runner:
```
docker model run hf.co/EryriLabs/Lightning-Herald-30B-A3B-GGUF:Q4_K_M
```

Lemonade

How to use EryriLabs/Lightning-Herald-30B-A3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull EryriLabs/Lightning-Herald-30B-A3B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Lightning-Herald-30B-A3B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use EryriLabs/Lightning-Herald-30B-A3B-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 EryriLabs/Lightning-Herald-30B-A3B-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 EryriLabs/Lightning-Herald-30B-A3B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use EryriLabs/Lightning-Herald-30B-A3B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf EryriLabs/Lightning-Herald-30B-A3B-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 "EryriLabs/Lightning-Herald-30B-A3B-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"

Lightning-Herald-30B-A3B GGUF

Lightning-Herald

GGUF builds of EryriLabs/Lightning-Herald-30B-A3B, my Hermes-Agent tune of NVIDIA Nemotron 3.5 Lightning (30B total, 3B active). The full model card, benchmarks and training details live on the main repo. This one is just the files.

Files

Quant	Size	Notes
IQ4_XS	19 GB	The exact file used in the before/after benchmark. Best fit for one 24GB card with a big context.
Q4_K_M	25 GB	Standard 4-bit. Needs two GPUs or some CPU offload on 24GB.
Q5_K_M	27 GB
Q6_K	35 GB
Q8_0	35 GB	Close to lossless.

All made with llama.cpp b10413 (convert_hf_to_gguf.py then llama-quantize) from the released BF16 weights. You need a llama.cpp build with Nemotron-H support, so b10413 or newer.

Run

llama-server -m Lightning-Herald-30B-A3B-IQ4_XS.gguf --jinja -ngl 999 -c 32768 --temp 1.0 --top-p 0.95

--jinja matters: it makes llama.cpp render the chat template with your tools and parse tool calls into structured tool_calls. The sampling above is NVIDIA's recommendation for the base model and what the benchmark used.

Point Hermes Agent at http://127.0.0.1:8080/v1. A config preset is on the main repo.

Licence

OpenMDW-1.1, inherited from the base. Training data was Apache 2.0.

Dwain Barnes, EryriLabs

Downloads last month: 539

GGUF

Model size

33B params

Architecture

nemotron_h_moe

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

Model tree for EryriLabs/Lightning-Herald-30B-A3B-GGUF

Base model

nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16

Finetuned

EryriLabs/Lightning-Herald-30B-A3B

Quantized

(1)

this model

Collection including EryriLabs/Lightning-Herald-30B-A3B-GGUF

Merges & MoE Experiments

Collection

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