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

Notebooks
Google Colab
Kaggle
Local Apps Settings

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

Install from WinGet (Windows)

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

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf julianmb/Ornith-1.0-35B-ROCmFPX-StrixHalo
# Run inference directly in the terminal:
./build/bin/llama cli -hf julianmb/Ornith-1.0-35B-ROCmFPX-StrixHalo

Use Docker

docker model run hf.co/julianmb/Ornith-1.0-35B-ROCmFPX-StrixHalo

Llama new
LM Studio
Jan

vLLM

How to use julianmb/Ornith-1.0-35B-ROCmFPX-StrixHalo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "julianmb/Ornith-1.0-35B-ROCmFPX-StrixHalo"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "julianmb/Ornith-1.0-35B-ROCmFPX-StrixHalo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/julianmb/Ornith-1.0-35B-ROCmFPX-StrixHalo

Ollama
How to use julianmb/Ornith-1.0-35B-ROCmFPX-StrixHalo with Ollama:
```
ollama run hf.co/julianmb/Ornith-1.0-35B-ROCmFPX-StrixHalo
```
Unsloth Desktop

How to use julianmb/Ornith-1.0-35B-ROCmFPX-StrixHalo with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf julianmb/Ornith-1.0-35B-ROCmFPX-StrixHalo

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": "julianmb/Ornith-1.0-35B-ROCmFPX-StrixHalo"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use julianmb/Ornith-1.0-35B-ROCmFPX-StrixHalo with Docker Model Runner:
```
docker model run hf.co/julianmb/Ornith-1.0-35B-ROCmFPX-StrixHalo
```

Lemonade

How to use julianmb/Ornith-1.0-35B-ROCmFPX-StrixHalo with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull julianmb/Ornith-1.0-35B-ROCmFPX-StrixHalo

Run and chat with the model

lemonade run user.Ornith-1.0-35B-ROCmFPX-StrixHalo-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use julianmb/Ornith-1.0-35B-ROCmFPX-StrixHalo with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf julianmb/Ornith-1.0-35B-ROCmFPX-StrixHalo

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 julianmb/Ornith-1.0-35B-ROCmFPX-StrixHalo

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use julianmb/Ornith-1.0-35B-ROCmFPX-StrixHalo with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf julianmb/Ornith-1.0-35B-ROCmFPX-StrixHalo

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 "julianmb/Ornith-1.0-35B-ROCmFPX-StrixHalo" \
  --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-1.0-35B ROCmFPX (Optimized for AMD Strix Halo)

Ornith-1.0-35B-ROCmFPX is a high-performance 35B mixture-of-experts model quantized and tuned specifically for local multi-agent workloads on AMD Ryzen AI Max+ 395 / Strix Halo (Radeon 8060S) using Vulkan and ROCmFPX.

🐙 Code Repository & Production Vulkan Runner

Full source code, production Python launcher, systemd daemon files, and pre-compiled Strix Halo Vulkan binaries are hosted on GitHub: 👉 julianmb/Ornith-1.0-35B-ROCmFPX-StrixHalo on GitHub

⚠️ Important: Engine & Runtime Compatibility

Note: ROCmFPX and ROCmFP4 GGUF models use custom experimental block tensor layouts. Standard stock llama.cpp, Ollama, or LM Studio cannot load ROCmFPX GGUFs out of the box.

To run these GGUF quants:

Use the Bundled Launcher: Use python3 run_server.py or ./run.sh from the GitHub Repository which ships with pre-compiled ROCmFPX Vulkan binaries.
Build from Source: Or build llama-server from the specialized ROCmFPX Fork.

Model Quants Included

Ornith-1.0-35B-ROCmFPX-Speed-StrixHalo.gguf (Primary Production Quant)
- Bitrate: ~4.70 bpw (Q4_0_ROCMFP4_COHERENT)
- File Size: ~19.7 GB
- Context Window: 262,144 tokens (256k)
- Tool Validity: 92.5% pass rate @ 4k context ($n=40$), 100.0% @ 16k context ($n=40$), 93.8% @ 16-agent burst concurrency.
- Performance: ~80 t/s single-stream decode, 115+ t/s aggregate decode across 16 concurrent agents.
Ornith-1.0-35B-ROCmFPX-Quality-StrixHalo.gguf (High-Precision Quant)
- Bitrate: ~6.50 bpw (Q6_0_ROCMFPX)
- File Size: ~32.2 GB
- Context Window: 262,144 tokens (256k)
- Tool Validity: 97.5% pass rate @ 4k context.

Quick Start with llama.cpp / llama-server (ROCmFPX Build)

# Start server with Vulkan backend tuned for AMD Strix Halo
llama-server \
  -m Ornith-1.0-35B-ROCmFPX-Speed-StrixHalo.gguf \
  -dev Vulkan0 \
  -ngl 99 \
  -fa on \
  -c 262144 \
  -np 8 \
  -ctk q8_0 \
  -ctv q8_0 \
  -b 4096 \
  -ub 1024 \
  -t 16 \
  --jinja \
  --mlock \
  --no-warmup \
  --no-context-shift \
  --cache-prompt \
  --cache-ram 65536 \
  --host 0.0.0.0 \
  --port 8089 \
  --api-key o4a-secret-key

OpenAI-compatible API endpoint will be live at http://127.0.0.1:8089/v1.

Hardware & System Requirements

Processor: AMD Ryzen AI Max+ 395 / 390 (Strix Halo)
GPU: Integrated AMD Radeon 8060S (gfx1151)
Memory: 64 GB or 128 GB LPDDR5X Unified RAM
Backend: Vulkan (Mesa RADV driver)

Benchmarks & Measured Performance

Metric	Single Agent (N=1)	8 Parallel Agents	16 Parallel Agents
Decode Speed (t/s)	80.98 t/s	137.69 t/s	115.16 t/s
Tool-Call Validity	100.0%	100.0%	93.8%
KV Quantization	`Q8_0`	`Q8_0`	`Q8_0`
Prompt Cache Hit TTFT	398 ms	398 ms	398 ms

Downloads last month: 285

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

We're not able to determine the quantization variants.

View all variants