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")

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

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/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FreedomAISVR/Ornith-1.0-35B-NVFP4-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 Desktop
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

Atomic Chat

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: 377

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

4-bit

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

Base model

ornith-ai/Ornith-1.0-35B

Quantized

(180)

this model