Instructions to use NANI-Nithin/Ornith-1.5-9B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use NANI-Nithin/Ornith-1.5-9B-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 NANI-Nithin/Ornith-1.5-9B-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf NANI-Nithin/Ornith-1.5-9B-GGUF:Q2_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf NANI-Nithin/Ornith-1.5-9B-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf NANI-Nithin/Ornith-1.5-9B-GGUF:Q2_K
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 NANI-Nithin/Ornith-1.5-9B-GGUF:Q2_K # Run inference directly in the terminal: ./llama-cli -hf NANI-Nithin/Ornith-1.5-9B-GGUF:Q2_K
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 NANI-Nithin/Ornith-1.5-9B-GGUF:Q2_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf NANI-Nithin/Ornith-1.5-9B-GGUF:Q2_K
Use Docker
docker model run hf.co/NANI-Nithin/Ornith-1.5-9B-GGUF:Q2_K
- LM Studio
- Jan
- vLLM
How to use NANI-Nithin/Ornith-1.5-9B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "NANI-Nithin/Ornith-1.5-9B-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": "NANI-Nithin/Ornith-1.5-9B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/NANI-Nithin/Ornith-1.5-9B-GGUF:Q2_K
- Ollama
How to use NANI-Nithin/Ornith-1.5-9B-GGUF with Ollama:
ollama run hf.co/NANI-Nithin/Ornith-1.5-9B-GGUF:Q2_K
- Unsloth Studio
How to use NANI-Nithin/Ornith-1.5-9B-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 NANI-Nithin/Ornith-1.5-9B-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 NANI-Nithin/Ornith-1.5-9B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for NANI-Nithin/Ornith-1.5-9B-GGUF to start chatting
- Pi
How to use NANI-Nithin/Ornith-1.5-9B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf NANI-Nithin/Ornith-1.5-9B-GGUF:Q2_K
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": "NANI-Nithin/Ornith-1.5-9B-GGUF:Q2_K" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use NANI-Nithin/Ornith-1.5-9B-GGUF with Docker Model Runner:
docker model run hf.co/NANI-Nithin/Ornith-1.5-9B-GGUF:Q2_K
- Lemonade
How to use NANI-Nithin/Ornith-1.5-9B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull NANI-Nithin/Ornith-1.5-9B-GGUF:Q2_K
Run and chat with the model
lemonade run user.Ornith-1.5-9B-GGUF-Q2_K
List all available models
lemonade list
- Hermes Agent
How to use NANI-Nithin/Ornith-1.5-9B-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 NANI-Nithin/Ornith-1.5-9B-GGUF:Q2_K
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 NANI-Nithin/Ornith-1.5-9B-GGUF:Q2_K
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use NANI-Nithin/Ornith-1.5-9B-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf NANI-Nithin/Ornith-1.5-9B-GGUF:Q2_K
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 "NANI-Nithin/Ornith-1.5-9B-GGUF:Q2_K" \ --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"
Ornith-1.5-9B GGUF
GGUF quantizations of https://huggingface.co/ornith-ai/Ornith-1.5-9B for use with llama.cpp, LM Studio, Ollama (GGUF import), KoboldCpp, and other GGUF-compatible runtimes.
About the Base Model
Ornith-1.5-9B is a 9B-parameter Qwen3.5-family model using a hybrid architecture consisting of:
- 32 transformer layers
- Hybrid linear attention (Gated DeltaNet) and full attention
- MRoPE positional encoding
- Dense architecture (no routed experts)
These GGUF files are text-only conversions intended for inference in llama.cpp-compatible software.
Conversion Notes
- Converted using upstream
llama.cppwith native Qwen3.5 support. - The model was converted directly to Q8_0 GGUF and all other quants were generated from that Q8_0 source.
- Importance matrix (imatrix) calibration was generated using WikiText-2 and applied to IQ quantizations.
- The original Hugging Face configuration advertises an MTP/next-token prediction layer that is not present in the released weights. GGUF metadata was patched so the layer count matches the actual tensors contained in the model.
Available Quantizations
Standard Quants
- Q2_K
- Q3_K_S
- Q3_K_M
- Q3_K_L
- Q4_0
- Q4_1
- Q4_K_S
- Q4_K_M
- Q5_K_S
- Q5_K_M
- Q6_K
- Q8_0
IQ Quants
- IQ2_M
- IQ3_XXS
- IQ3_XS
- IQ3_M
- IQ4_XS
- IQ4_NL
Quant Recommendations
| Quant | Recommended Use |
|---|---|
| IQ2_M / Q2_K | Maximum memory savings |
| Q3_K_M | Very low RAM systems |
| Q4_K_M | Best balance of quality and size |
| Q5_K_M | High-quality daily use |
| Q6_K | Near-original quality |
| Q8_0 | Highest quality quant available in this repository |
Usage with llama.cpp
./llama-cli \
-m Ornith-1.5-9B-Q4_K_M.gguf \
-p "Explain quantum computing in simple terms."
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Model tree for NANI-Nithin/Ornith-1.5-9B-GGUF
Base model
ornith-ai/Ornith-1.5-9B