Instructions to use prithivMLmods/Ornith-1.5-9B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Ornith-1.5-9B-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="prithivMLmods/Ornith-1.5-9B-GGUF") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("prithivMLmods/Ornith-1.5-9B-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use prithivMLmods/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 prithivMLmods/Ornith-1.5-9B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf prithivMLmods/Ornith-1.5-9B-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 prithivMLmods/Ornith-1.5-9B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf prithivMLmods/Ornith-1.5-9B-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 prithivMLmods/Ornith-1.5-9B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf prithivMLmods/Ornith-1.5-9B-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 prithivMLmods/Ornith-1.5-9B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf prithivMLmods/Ornith-1.5-9B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/prithivMLmods/Ornith-1.5-9B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use prithivMLmods/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 "prithivMLmods/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": "prithivMLmods/Ornith-1.5-9B-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/prithivMLmods/Ornith-1.5-9B-GGUF:Q4_K_M
- SGLang
How to use prithivMLmods/Ornith-1.5-9B-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 "prithivMLmods/Ornith-1.5-9B-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": "prithivMLmods/Ornith-1.5-9B-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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 "prithivMLmods/Ornith-1.5-9B-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": "prithivMLmods/Ornith-1.5-9B-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Ollama
How to use prithivMLmods/Ornith-1.5-9B-GGUF with Ollama:
ollama run hf.co/prithivMLmods/Ornith-1.5-9B-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use prithivMLmods/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 prithivMLmods/Ornith-1.5-9B-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": "prithivMLmods/Ornith-1.5-9B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use prithivMLmods/Ornith-1.5-9B-GGUF with Docker Model Runner:
docker model run hf.co/prithivMLmods/Ornith-1.5-9B-GGUF:Q4_K_M
- Lemonade
How to use prithivMLmods/Ornith-1.5-9B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull prithivMLmods/Ornith-1.5-9B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Ornith-1.5-9B-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use prithivMLmods/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 prithivMLmods/Ornith-1.5-9B-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 prithivMLmods/Ornith-1.5-9B-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use prithivMLmods/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 prithivMLmods/Ornith-1.5-9B-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 "prithivMLmods/Ornith-1.5-9B-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"
Ornith-1.5-9B-GGUF
Ornith-1.5-9B is an open-weight, 9-billion parameter dense reasoning model released under the MIT license by Ornith AI, engineered for single-GPU inference and mobile edge deployment while advancing end-to-end autonomous self-improvement. Built on architectural lineages stemming from Qwen3.5 and Gemma-4 with extensive continued pre-training, mid-training, and reinforcement learning, the model transitions away from static human-curated datasets by operating an internal self-improvement loop that dynamically synthesizes new training tasks, discovers effective execution harnesses, and optimizes solution rollouts. It natively functions as a chain-of-thought reasoner—separating intermediate
<think>deliberations from final outputs—while providing structured, XML-parsed tool calling compatible with OpenAI APIs and major agent runtimes like vLLM, SGLang, Ollama, and llama.cpp. With a native context window of 262,144 tokens expandable up to 1M tokens via YaRN RoPE scaling, Ornith-1.5-9B demonstrates remarkable coding, reasoning, and tool-use performance that rivals substantially larger models, achieving high marks across demanding agentic benchmarks including SWE-bench Verified (70.6%), GPQA Diamond (86.4%), Terminal-Bench 2.1, and MCP-Atlas.
Model Files
| File Name | Quant Type | File Size | File Link |
|---|---|---|---|
| Ornith-1.5-9B.BF16.gguf | BF16 | 17.9 GB | Download |
| Ornith-1.5-9B.F16.gguf | F16 | 17.9 GB | Download |
| Ornith-1.5-9B.Q3_K_L.gguf | Q3_K_L | 4.93 GB | Download |
| Ornith-1.5-9B.Q3_K_M.gguf | Q3_K_M | 4.62 GB | Download |
| Ornith-1.5-9B.Q3_K_S.gguf | Q3_K_S | 4.26 GB | Download |
| Ornith-1.5-9B.Q4_0.gguf | Q4_0 | 5.31 GB | Download |
| Ornith-1.5-9B.Q4_K_M.gguf | Q4_K_M | 5.63 GB | Download |
| Ornith-1.5-9B.Q4_K_S.gguf | Q4_K_S | 5.35 GB | Download |
| Ornith-1.5-9B.Q5_0.gguf | Q5_0 | 6.31 GB | Download |
| Ornith-1.5-9B.Q5_K_M.gguf | Q5_K_M | 6.47 GB | Download |
| Ornith-1.5-9B.Q5_K_S.gguf | Q5_K_S | 6.31 GB | Download |
| Ornith-1.5-9B.Q6_K.gguf | Q6_K | 7.36 GB | Download |
| Ornith-1.5-9B.Q8_0.gguf | Q8_0 | 9.53 GB | Download |
| Ornith-1.5-9B.mmproj-bf16.gguf | mmproj-bf16 | 922 MB | Download |
| Ornith-1.5-9B.mmproj-f16.gguf | mmproj-f16 | 922 MB | Download |
| Ornith-1.5-9B.mmproj-q8_0.gguf | mmproj-q8_0 | 624 MB | Download |
llama.cpp
LLM inference in C/C++ — https://github.com/ggml-org/llama.cpp
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Model tree for prithivMLmods/Ornith-1.5-9B-GGUF
Base model
ornith-ai/Ornith-1.5-9B