Instructions to use ornith-ai/Ornith-1.5-35B-A3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ornith-ai/Ornith-1.5-35B-A3B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ornith-ai/Ornith-1.5-35B-A3B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ornith-ai/Ornith-1.5-35B-A3B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ornith-ai/Ornith-1.5-35B-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 ornith-ai/Ornith-1.5-35B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ornith-ai/Ornith-1.5-35B-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 ornith-ai/Ornith-1.5-35B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ornith-ai/Ornith-1.5-35B-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 ornith-ai/Ornith-1.5-35B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ornith-ai/Ornith-1.5-35B-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 ornith-ai/Ornith-1.5-35B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ornith-ai/Ornith-1.5-35B-A3B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ornith-ai/Ornith-1.5-35B-A3B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use ornith-ai/Ornith-1.5-35B-A3B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ornith-ai/Ornith-1.5-35B-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": "ornith-ai/Ornith-1.5-35B-A3B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ornith-ai/Ornith-1.5-35B-A3B-GGUF:Q4_K_M

SGLang

How to use ornith-ai/Ornith-1.5-35B-A3B-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ornith-ai/Ornith-1.5-35B-A3B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ornith-ai/Ornith-1.5-35B-A3B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ornith-ai/Ornith-1.5-35B-A3B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ornith-ai/Ornith-1.5-35B-A3B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use ornith-ai/Ornith-1.5-35B-A3B-GGUF with Ollama:
```
ollama run hf.co/ornith-ai/Ornith-1.5-35B-A3B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use ornith-ai/Ornith-1.5-35B-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 ornith-ai/Ornith-1.5-35B-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": "ornith-ai/Ornith-1.5-35B-A3B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ornith-ai/Ornith-1.5-35B-A3B-GGUF with Docker Model Runner:
```
docker model run hf.co/ornith-ai/Ornith-1.5-35B-A3B-GGUF:Q4_K_M
```

Lemonade

How to use ornith-ai/Ornith-1.5-35B-A3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ornith-ai/Ornith-1.5-35B-A3B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Ornith-1.5-35B-A3B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use ornith-ai/Ornith-1.5-35B-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 ornith-ai/Ornith-1.5-35B-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 ornith-ai/Ornith-1.5-35B-A3B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ornith-ai/Ornith-1.5-35B-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 ornith-ai/Ornith-1.5-35B-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 "ornith-ai/Ornith-1.5-35B-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"

MTP Support

by DefaultDF - opened Aug 19

Discussion

DefaultDF

Aug 19

Will it be able to support MTP? On the standard Qwen 3.6 35A3B model, I managed to achieve 100t/s using MTP... But Ornith 1.0 could only squeeze out 40t/s on the same context.

hgeist

Aug 19

Someone on Reddit read it out and it had MTP headers in it. Just try it.

Avifenesh

Aug 19

Hugging Face CLI

The MTP head survives quantization — I published an NVFP4 GGUF (20.2 GB) with the head kept in-file, plus a standalone FR-Spec vocab-masked draft and the ranks: https://huggingface.co/Avifenesh/Ornith-1.5-35B-A3B-NVFP4-MTP-GGUF. Note NVFP4 isn't upstream llama.cpp — it runs on memra (https://github.com/avifenesh/memra) or the llama.cpp NVFP4 branch linked on the card, and measured acceptance on this head runs lower than Qwen3.6-35B-A3B's (numbers on the card), consistent with what you saw on 1.0.

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