Instructions to use protoLabsAI/Ornith-1.0-9B-MTP-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use protoLabsAI/Ornith-1.0-9B-MTP-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="protoLabsAI/Ornith-1.0-9B-MTP-GGUF",
	filename="Ornith-1.0-9B-MTP-BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use protoLabsAI/Ornith-1.0-9B-MTP-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 protoLabsAI/Ornith-1.0-9B-MTP-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf protoLabsAI/Ornith-1.0-9B-MTP-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 protoLabsAI/Ornith-1.0-9B-MTP-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf protoLabsAI/Ornith-1.0-9B-MTP-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 protoLabsAI/Ornith-1.0-9B-MTP-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf protoLabsAI/Ornith-1.0-9B-MTP-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 protoLabsAI/Ornith-1.0-9B-MTP-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf protoLabsAI/Ornith-1.0-9B-MTP-GGUF:Q4_K_M

Use Docker

docker model run hf.co/protoLabsAI/Ornith-1.0-9B-MTP-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use protoLabsAI/Ornith-1.0-9B-MTP-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "protoLabsAI/Ornith-1.0-9B-MTP-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": "protoLabsAI/Ornith-1.0-9B-MTP-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/protoLabsAI/Ornith-1.0-9B-MTP-GGUF:Q4_K_M

Ollama
How to use protoLabsAI/Ornith-1.0-9B-MTP-GGUF with Ollama:
```
ollama run hf.co/protoLabsAI/Ornith-1.0-9B-MTP-GGUF:Q4_K_M
```

Unsloth Studio

How to use protoLabsAI/Ornith-1.0-9B-MTP-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for protoLabsAI/Ornith-1.0-9B-MTP-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for protoLabsAI/Ornith-1.0-9B-MTP-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for protoLabsAI/Ornith-1.0-9B-MTP-GGUF to start chatting

How to use protoLabsAI/Ornith-1.0-9B-MTP-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf protoLabsAI/Ornith-1.0-9B-MTP-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "protoLabsAI/Ornith-1.0-9B-MTP-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use protoLabsAI/Ornith-1.0-9B-MTP-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 protoLabsAI/Ornith-1.0-9B-MTP-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 protoLabsAI/Ornith-1.0-9B-MTP-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use protoLabsAI/Ornith-1.0-9B-MTP-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf protoLabsAI/Ornith-1.0-9B-MTP-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 "protoLabsAI/Ornith-1.0-9B-MTP-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"

Docker Model Runner
How to use protoLabsAI/Ornith-1.0-9B-MTP-GGUF with Docker Model Runner:
```
docker model run hf.co/protoLabsAI/Ornith-1.0-9B-MTP-GGUF:Q4_K_M
```

Lemonade

How to use protoLabsAI/Ornith-1.0-9B-MTP-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull protoLabsAI/Ornith-1.0-9B-MTP-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Ornith-1.0-9B-MTP-GGUF-Q4_K_M

List all available models

lemonade list

Iq quants

by koyukira - opened 9 days ago

Discussion

koyukira

9 days ago

Now, Im test the mtp of q8.

Could you please create iq quants? is that possible to create?

thankyou for the hard work.

artificial-citizen

protoLabsAI org 8 days ago

yep, added to files

bleaki

6 days ago

Is there a reason I usually see IQ quants stop at IQ4? I prefer IQ's for when size is an issue, but for a 9b model I'd love to see something like IQ8 / 6.

artificial-citizen

protoLabsAI org 6 days ago

So Q6_K and Q8_0 are the IQ6/IQ8 you're picturing, they just don't carry the IQ prefix because at that bit budget the importance-codebook buys you nothing. That's the reason you never see them. I could load them up, but they wouldn't be worth the disk space.

bleaki

6 days ago

Ah! Thank you for the explanation, does this change at all with large parameter models or does it essentially always end up at the same point?

artificial-citizen

protoLabsAI org 6 days ago

So the mapping holds permanently regardless of model size 6-bit → Q6_K. 8-bit → Q8_0. Those are the 6-bit and 8-bit quants; they just don't wear an "IQ" badge because at that bit budget the IQ machinery is dead weight.

bleaki

6 days ago

Thank you!

artificial-citizen changed discussion status to closed 6 days ago

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