Instructions to use lmcoleman/Ornith-1.5-35B-A3B-ROCmFPX-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 lmcoleman/Ornith-1.5-35B-A3B-ROCmFPX-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 lmcoleman/Ornith-1.5-35B-A3B-ROCmFPX-GGUF:F16 # Run inference directly in the terminal: llama cli -hf lmcoleman/Ornith-1.5-35B-A3B-ROCmFPX-GGUF:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf lmcoleman/Ornith-1.5-35B-A3B-ROCmFPX-GGUF:F16 # Run inference directly in the terminal: llama cli -hf lmcoleman/Ornith-1.5-35B-A3B-ROCmFPX-GGUF:F16
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 lmcoleman/Ornith-1.5-35B-A3B-ROCmFPX-GGUF:F16 # Run inference directly in the terminal: ./llama-cli -hf lmcoleman/Ornith-1.5-35B-A3B-ROCmFPX-GGUF:F16
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 lmcoleman/Ornith-1.5-35B-A3B-ROCmFPX-GGUF:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf lmcoleman/Ornith-1.5-35B-A3B-ROCmFPX-GGUF:F16
Use Docker
docker model run hf.co/lmcoleman/Ornith-1.5-35B-A3B-ROCmFPX-GGUF:F16
- LM Studio
- Jan
- vLLM
How to use lmcoleman/Ornith-1.5-35B-A3B-ROCmFPX-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lmcoleman/Ornith-1.5-35B-A3B-ROCmFPX-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": "lmcoleman/Ornith-1.5-35B-A3B-ROCmFPX-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/lmcoleman/Ornith-1.5-35B-A3B-ROCmFPX-GGUF:F16
- Ollama
How to use lmcoleman/Ornith-1.5-35B-A3B-ROCmFPX-GGUF with Ollama:
ollama run hf.co/lmcoleman/Ornith-1.5-35B-A3B-ROCmFPX-GGUF:F16
- Unsloth Studio
How to use lmcoleman/Ornith-1.5-35B-A3B-ROCmFPX-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 lmcoleman/Ornith-1.5-35B-A3B-ROCmFPX-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 lmcoleman/Ornith-1.5-35B-A3B-ROCmFPX-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for lmcoleman/Ornith-1.5-35B-A3B-ROCmFPX-GGUF to start chatting
- Pi
How to use lmcoleman/Ornith-1.5-35B-A3B-ROCmFPX-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf lmcoleman/Ornith-1.5-35B-A3B-ROCmFPX-GGUF:F16
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": "lmcoleman/Ornith-1.5-35B-A3B-ROCmFPX-GGUF:F16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use lmcoleman/Ornith-1.5-35B-A3B-ROCmFPX-GGUF with Docker Model Runner:
docker model run hf.co/lmcoleman/Ornith-1.5-35B-A3B-ROCmFPX-GGUF:F16
- Lemonade
How to use lmcoleman/Ornith-1.5-35B-A3B-ROCmFPX-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull lmcoleman/Ornith-1.5-35B-A3B-ROCmFPX-GGUF:F16
Run and chat with the model
lemonade run user.Ornith-1.5-35B-A3B-ROCmFPX-GGUF-F16
List all available models
lemonade list
- Hermes Agent
How to use lmcoleman/Ornith-1.5-35B-A3B-ROCmFPX-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 lmcoleman/Ornith-1.5-35B-A3B-ROCmFPX-GGUF:F16
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 lmcoleman/Ornith-1.5-35B-A3B-ROCmFPX-GGUF:F16
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use lmcoleman/Ornith-1.5-35B-A3B-ROCmFPX-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf lmcoleman/Ornith-1.5-35B-A3B-ROCmFPX-GGUF:F16
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 "lmcoleman/Ornith-1.5-35B-A3B-ROCmFPX-GGUF:F16" \ --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-1.5-35B-A3B โ ROCmFPX renders
MagicQuant's per-group scheme assignments for
ornith-ai/Ornith-1.5-35B-A3B
rendered into ROCmFPX tensor types (q*_0_rocmfpx), a quant family aimed
at AMD ROCm inference. Same search that produced the
MagicQuant GGUFs,
different render target.
Pins: MagicQuant ed17991, Foundry 2f99202.
โ Engine requirement โ read before downloading
These files use ROCmFPX tensor types that stock llama.cpp cannot load.
They require a ROCmFPX fork build. A build that is too old fails with
invalid ggml type on load; the types are not a stock-llama.cpp feature and
no amount of rebuilding mainline will help. If you are not already running a
ROCmFPX fork, you want the MagicQuant repo linked above instead.
Files
| file | size |
|---|---|
Ornith-1.5-35B-A3B-ROCMFPX-MQ-Q4.gguf |
20.50 GiB |
Ornith-1.5-35B-A3B-ROCMFPX-MQ-Q6.gguf |
28.57 GiB |
mmproj-Ornith-1.5-35B-A3B-f16.gguf |
0.86 GiB |
Both passed the pipeline's perplexity smoke gate (6.55 and 6.50 respectively).
Those smoke numbers are not comparable to the MagicQuant repo's PPL table. The smoke gate is a short run against a different corpus path than the search used, so the scale differs for instrument reasons, not quality reasons. No matched-instrument comparison between these ROCmFPX renders and their MagicQuant counterparts has been run, so this repo makes no claim about which is better. If that comparison matters to you, measure both yourself under one instrument.
What is deliberately absent
- No Q5. The pipeline's band guard refused it: rendering MagicQuant's Q5 configuration into ROCmFPX types predicts 26.87 GiB against a 66.13 GiB baseline โ a 0.406 ratio, which is the Q6 band, not Q5. Publishing it as "Q5" would have mislabelled it, so it was not built.
- No
Q6_0_ROCMFPX_AGENTvariant. It built, then segfaulted under perplexity evaluation (exited -11). The pipeline quarantined it automatically and aborted before upload. It is a crash, not a quality miss, and it is not published because it does not work.
Both absences are disclosed rather than quietly dropped, which is the point.
Notes
- Vision requires the
mmprojfile alongside the model. - Chat template embedded and verified present before upload; vocab metadata
(
tokenizer.ggml.token_type) is INT32 per spec. - Built and measured on an AMD Strix Halo (Ryzen AI MAX+ 395, gfx1151) host.
- Downloads last month
- 190
We're not able to determine the quantization variants.
Model tree for lmcoleman/Ornith-1.5-35B-A3B-ROCmFPX-GGUF
Base model
ornith-ai/Ornith-1.5-35B-A3B