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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use gsrunion/Ornith-1.0-35B-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 gsrunion/Ornith-1.0-35B-ROCmFPX-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
llama cli -hf gsrunion/Ornith-1.0-35B-ROCmFPX-GGUF:Q4_0_ROCMFP

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf gsrunion/Ornith-1.0-35B-ROCmFPX-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
llama cli -hf gsrunion/Ornith-1.0-35B-ROCmFPX-GGUF:Q4_0_ROCMFP

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 gsrunion/Ornith-1.0-35B-ROCmFPX-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
./llama-cli -hf gsrunion/Ornith-1.0-35B-ROCmFPX-GGUF:Q4_0_ROCMFP

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 gsrunion/Ornith-1.0-35B-ROCmFPX-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
./build/bin/llama-cli -hf gsrunion/Ornith-1.0-35B-ROCmFPX-GGUF:Q4_0_ROCMFP

Use Docker

docker model run hf.co/gsrunion/Ornith-1.0-35B-ROCmFPX-GGUF:Q4_0_ROCMFP

LM Studio
Jan

vLLM

How to use gsrunion/Ornith-1.0-35B-ROCmFPX-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/gsrunion/Ornith-1.0-35B-ROCmFPX-GGUF:Q4_0_ROCMFP

Ollama
How to use gsrunion/Ornith-1.0-35B-ROCmFPX-GGUF with Ollama:
```
ollama run hf.co/gsrunion/Ornith-1.0-35B-ROCmFPX-GGUF:Q4_0_ROCMFP
```

Unsloth Studio

How to use gsrunion/Ornith-1.0-35B-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 gsrunion/Ornith-1.0-35B-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 gsrunion/Ornith-1.0-35B-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 gsrunion/Ornith-1.0-35B-ROCmFPX-GGUF to start chatting

How to use gsrunion/Ornith-1.0-35B-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 gsrunion/Ornith-1.0-35B-ROCmFPX-GGUF:Q4_0_ROCMFP

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": "gsrunion/Ornith-1.0-35B-ROCmFPX-GGUF:Q4_0_ROCMFP"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use gsrunion/Ornith-1.0-35B-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 gsrunion/Ornith-1.0-35B-ROCmFPX-GGUF:Q4_0_ROCMFP

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 gsrunion/Ornith-1.0-35B-ROCmFPX-GGUF:Q4_0_ROCMFP

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use gsrunion/Ornith-1.0-35B-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 gsrunion/Ornith-1.0-35B-ROCmFPX-GGUF:Q4_0_ROCMFP

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 "gsrunion/Ornith-1.0-35B-ROCmFPX-GGUF:Q4_0_ROCMFP" \
  --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 gsrunion/Ornith-1.0-35B-ROCmFPX-GGUF with Docker Model Runner:
```
docker model run hf.co/gsrunion/Ornith-1.0-35B-ROCmFPX-GGUF:Q4_0_ROCMFP
```

Lemonade

How to use gsrunion/Ornith-1.0-35B-ROCmFPX-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull gsrunion/Ornith-1.0-35B-ROCmFPX-GGUF:Q4_0_ROCMFP

Run and chat with the model

lemonade run user.Ornith-1.0-35B-ROCmFPX-GGUF-Q4_0_ROCMFP

List all available models

lemonade list

Ornith-1.0-35B — ROCmFPX quants for AMD Strix Halo

ROCmFPX-family GGUF quantization of deepreinforce-ai/Ornith-1.0-35B (Qwen3.5-MoE architecture, multimodal), produced for AMD Strix Halo (Ryzen AI Max, gfx1151) and the hal0 home inference platform.

⚠️ These files require the Hal0ai/Hal0_ROCmFPX llama.cpp fork (or the ghcr.io/hal0ai/hal0-rocmfpx container image that hal0 uses). Stock llama.cpp will reject the tensor types (invalid ggml type 101).

See also the companion repo: gsrunion/Ornith-1.0-9B-ROCmFPX-GGUF.

Files

File	Quant	BPW	Size	Notes
`Ornith-1.0-35B-Q4_0_ROCMFP4_STRIX_LEAN.gguf`	Q4_0_ROCMFP4_STRIX_LEAN	~4.3	18.6 GB	Size-biased Strix recipe — fits comfortably in a 64 GB GPU carve-out with long context
`mmproj-BF16.gguf`	BF16	—	0.90 GB	Vision projector (Ornith is multimodal) — load alongside the quant
`imatrix_unsloth.gguf_file`	—	—	184 MB	Importance matrix used for calibration (from unsloth, included for reproducibility)

Measured performance

On AMD Ryzen AI Max+ 395 (Strix Halo, 128 GB unified LPDDR5X, ROCm backend, hal0-rocmfpx image, 64K ctx):

Q4_0_ROCMFP4_STRIX_LEAN: ~66 tok/s decode — the MoE's small active-parameter set decodes faster than the dense 9B (42 tok/s) despite the 3.7× file size.

How it was made

BF16 GGUF source and imatrix from unsloth/Ornith-1.0-35B-GGUF, quantized with the Hal0_ROCmFPX fork's llama-quantize:

llama-quantize --imatrix imatrix_unsloth.gguf_file \
  Ornith-1.0-35B-BF16.gguf Ornith-1.0-35B-Q4_0_ROCMFP4_STRIX_LEAN.gguf \
  Q4_0_ROCMFP4_STRIX_LEAN

Serving

Directly with the fork's llama-server (as done on hal0 boxes):

llama-server -m Ornith-1.0-35B-Q4_0_ROCMFP4_STRIX_LEAN.gguf \
  --mmproj mmproj-BF16.gguf -ngl 999 -fa on --jinja -c 65536

Or on hal0: download into the model store, register via add-from-path, then hal0 slot create <name> --type llm --hardware rocm --model <id> and load via a long-lived curl.

Credits

Base model: deepreinforce-ai — Ornith-1.0-35B (MIT)
BF16 GGUF conversion + imatrix: unsloth
ROCmFPX quant formats + fork: Hal0ai

Downloads last month: 378

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

4-bit

8-bit

View +1 variant

Model tree for gsrunion/Ornith-1.0-35B-ROCmFPX-GGUF

Base model

deepreinforce-ai/Ornith-1.0-35B

Quantized

(166)

this model