Instructions to use FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF", dtype="auto")

llama-cpp-python

How to use FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF",
	filename="ornith-1.0-35b-nvfp4.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 FreedomAISVR/Ornith-1.0-35B-NVFP4-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 FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
llama cli -hf FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF:NVFP4

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
llama cli -hf FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF:NVFP4

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 FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
./llama-cli -hf FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF:NVFP4

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 FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
./build/bin/llama-cli -hf FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF:NVFP4

Use Docker

docker model run hf.co/FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF:NVFP4

LM Studio
Jan

vLLM

How to use FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FreedomAISVR/Ornith-1.0-35B-NVFP4-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": "FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF:NVFP4

SGLang

How to use FreedomAISVR/Ornith-1.0-35B-NVFP4-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 "FreedomAISVR/Ornith-1.0-35B-NVFP4-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": "FreedomAISVR/Ornith-1.0-35B-NVFP4-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 "FreedomAISVR/Ornith-1.0-35B-NVFP4-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": "FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF with Ollama:
```
ollama run hf.co/FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF:NVFP4
```

Unsloth Studio

How to use FreedomAISVR/Ornith-1.0-35B-NVFP4-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 FreedomAISVR/Ornith-1.0-35B-NVFP4-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 FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF to start chatting

How to use FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF:NVFP4

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": "FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF:NVFP4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use FreedomAISVR/Ornith-1.0-35B-NVFP4-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 FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF:NVFP4

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 FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF:NVFP4

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF:NVFP4

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 "FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF:NVFP4" \
  --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 FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF with Docker Model Runner:
```
docker model run hf.co/FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF:NVFP4
```

Lemonade

How to use FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF:NVFP4

Run and chat with the model

lemonade run user.Ornith-1.0-35B-NVFP4-GGUF-NVFP4

List all available models

lemonade list

Ornith 1.0 35B — NVFP4 GGUF

NVFP4 quantization of deepreinforce-ai/Ornith-1.0-35B, a 35B parameter Qwen3.5 MoE coding agent with 256 experts (8 active per token).

About the Model

Ornith-1.0-35B is the lightweight member of the Ornith family, designed for efficient single-GPU deployment.

State-of-the-Art Coding Agents: Post-trained on top of Qwen 3.5, achieving state-of-the-art performance among open-source models
Self-Improving Training Framework: Ornith-1.0 employs RL to learn to generate not only solution rollouts, but also the scaffold that drives those rollouts
35B total parameters with 8B active per token (256 experts, 8 active)
40-layer MoE architecture with sliding + full attention hybrid
262K context window
MIT License — globally accessible, no regional limitations

Architecture

Text model: Qwen3.5 MoE — 40 layers, 2048 hidden, 256 experts (8 active/token)
Vocabulary: 248,320 tokens

Quantization

Quantized from the BF16 safetensors using llama.cpp (build 537).

NVFP4 (NVIDIA FP4) uses 4-bit floating point quantization optimized for NVIDIA Blackwell GPUs.

Files

File	Size	Description
`ornith-1.0-35b-nvfp4.gguf`	~18.4 GB	NVFP4 quantized model

Usage

llama-server \
  -m ornith-1.0-35b-nvfp4.gguf \
  -ngl 99 \
  --host 0.0.0.0 \
  --port 8080

Hardware Requirements

Minimum: 20 GB VRAM
Recommended: 24+ GB VRAM for full GPU offload

License

MIT

Downloads last month: 670

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

4-bit

Model tree for FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF

Base model

deepreinforce-ai/Ornith-1.0-35B

Quantized

(135)

this model