Instructions to use protoLabsAI/Ornith-1.0-9B-MTP-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 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 Desktop

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 @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": "protoLabsAI/Ornith-1.0-9B-MTP-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Hermes Agent

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

OpenClaw

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"

MTP

by wepiqx - opened Jun 28

Discussion

wepiqx

Jun 28

Hi,love your work but how did you manage put nextn tensors? because i tried myself with the python script and this is only what i got

and the separate MTP (2 gb) works just fine

artificial-citizen

protoLabsAI org Jun 28

•

edited Jun 28

Hello, thanks for the support.

That expected 442 got 427 gap is exactly 15 tensors, which is the mtp.* head. You're converting the bare base. deepreinforce-ai/Ornith-1.0-9B keeps mtp_num_hidden_layers: 1 in its config but ships none of the mtp.* weights, so the converter writes block_count = 33 into the metadata (declaring the blk.32 MTP layer) but has nothing to fill it, and the load expects 442 while the file has 427.

Fix: graft the head into the trunk before converting, then convert with no --mtp flag:

hf download protoLabsAI/Ornith-1.0-9B-MTP --local-dir ./mtp-head
python ./mtp-head/recipe/graft.py \
  --donor ./mtp-head --target deepreinforce-ai/Ornith-1.0-9B \
  --out ./ornith-9b-mtp --dtype bfloat16
python convert_hf_to_gguf.py ./ornith-9b-mtp --outfile ornith-9b-mtp-BF16.gguf --outtype bf16

Heads up: only 4 of the 15 head tensors are named blk.32.nextn.* (eh_proj, enorm, hnorm, shared_head_norm); the other 11 land as ordinary blk.32.* (attn/ffn/norms), so grepping nextn shows 4, not 15, but the head is complete.

And since your standalone 2 GB draft already works, you can skip grafting entirely: just run the base GGUF with --model-draft mtp-ornith-9b-mtp-kl-Q8_0.gguf --spec-type draft-mtp. Same result.

artificial-citizen

protoLabsAI org Jun 28

I've added a troubleshooting section to the readme so others don't hit the same issue.

artificial-citizen changed discussion status to closed Jun 28

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