Instructions to use gsrunion/Ornith-1.0-9B-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-1.0-9B-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-1.0-9B-ROCmFPX-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
llama cli -hf gsrunion/Ornith-1.0-9B-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-1.0-9B-ROCmFPX-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
llama cli -hf gsrunion/Ornith-1.0-9B-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-1.0-9B-ROCmFPX-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
./llama-cli -hf gsrunion/Ornith-1.0-9B-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-1.0-9B-ROCmFPX-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
./build/bin/llama-cli -hf gsrunion/Ornith-1.0-9B-ROCmFPX-GGUF:Q4_0_ROCMFP

Use Docker

docker model run hf.co/gsrunion/Ornith-1.0-9B-ROCmFPX-GGUF:Q4_0_ROCMFP

LM Studio
Jan

vLLM

How to use gsrunion/Ornith-1.0-9B-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-1.0-9B-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-1.0-9B-ROCmFPX-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/gsrunion/Ornith-1.0-9B-ROCmFPX-GGUF:Q4_0_ROCMFP

Ollama
How to use gsrunion/Ornith-1.0-9B-ROCmFPX-GGUF with Ollama:
```
ollama run hf.co/gsrunion/Ornith-1.0-9B-ROCmFPX-GGUF:Q4_0_ROCMFP
```

Unsloth Studio

How to use gsrunion/Ornith-1.0-9B-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-1.0-9B-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-1.0-9B-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-1.0-9B-ROCmFPX-GGUF to start chatting

How to use gsrunion/Ornith-1.0-9B-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-1.0-9B-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-1.0-9B-ROCmFPX-GGUF:Q4_0_ROCMFP"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use gsrunion/Ornith-1.0-9B-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-1.0-9B-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-1.0-9B-ROCmFPX-GGUF:Q4_0_ROCMFP

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use gsrunion/Ornith-1.0-9B-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-1.0-9B-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-1.0-9B-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-1.0-9B-ROCmFPX-GGUF with Docker Model Runner:
```
docker model run hf.co/gsrunion/Ornith-1.0-9B-ROCmFPX-GGUF:Q4_0_ROCMFP
```

Lemonade

How to use gsrunion/Ornith-1.0-9B-ROCmFPX-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull gsrunion/Ornith-1.0-9B-ROCmFPX-GGUF:Q4_0_ROCMFP

Run and chat with the model

lemonade run user.Ornith-1.0-9B-ROCmFPX-GGUF-Q4_0_ROCMFP

List all available models

lemonade list

Ornith-1.0-9B-ROCmFPX-GGUF / README.md

gsrunion

Add ROCmFPX quants (STRIX_LEAN/STRIX/Q6_QUALITY/Q8_AGENT) + mmproj + imatrix

ed3faa4 verified 11 days ago

preview code

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metadata

license: mit
language:
  - en
base_model: deepreinforce-ai/Ornith-1.0-9B
base_model_relation: quantized
pipeline_tag: text-generation
library_name: gguf
tags:
  - gguf
  - rocmfp4
  - rocmfpx
  - quantized
  - imatrix
  - amd
  - rocm
  - strix-halo
  - agent
  - hal0

Ornith-1.0-9B — ROCmFPX quants for AMD Strix Halo

ROCmFPX-family GGUF quantizations of deepreinforce-ai/Ornith-1.0-9B, 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).

Files

File	Quant	BPW	Size	Notes
`Ornith-1.0-9B-Q4_0_ROCMFP4_STRIX_LEAN.gguf`	Q4_0_ROCMFP4_STRIX_LEAN	4.42	4.96 GB	Size-biased Strix recipe, Q5_K token embeddings — best size/speed
`Ornith-1.0-9B-Q4_0_ROCMFP4_STRIX.gguf`	Q4_0_ROCMFP4_STRIX	4.54	5.09 GB	Quality-biased attention K/V recipe
`Ornith-1.0-9B-Q6_0_ROCMFPX_STRIX_QUALITY.gguf`	Q6_0_ROCMFPX_STRIX_QUALITY	7.50	8.41 GB	FP6 bulk + Q8 protected tensors — near-lossless daily driver
`Ornith-1.0-9B-Q8_0_ROCMFPX_AGENT.gguf`	Q8_0_ROCMFPX_AGENT	8.41	9.42 GB	Agent profile: protects embeddings, attn Q/K/V/O and select FFN tensors for tool-calling / JSON fidelity
`mmproj-BF16.gguf`	BF16	—	0.92 GB	Vision projector (Ornith is multimodal) — load alongside any quant
`imatrix_unsloth.gguf_file`	—	—	5 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):

Q4_0_ROCMFP4_STRIX_LEAN: 42 tok/s decode, 317 tok/s prompt processing (32K ctx slot) — ≈200 GB/s effective bandwidth, near the platform's memory-bandwidth ceiling.

How they were made

BF16 GGUF source and imatrix from unsloth/Ornith-1.0-9B-GGUF, quantized with the Hal0_ROCmFPX fork's llama-quantize:

llama-quantize --imatrix imatrix_unsloth.gguf_file \
  Ornith-1.0-9B-BF16.gguf Ornith-1.0-9B-Q4_0_ROCMFP4_STRIX_LEAN.gguf \
  Q4_0_ROCMFP4_STRIX_LEAN

(same invocation per type for the other three)

Serving on hal0

hal0 model pull gsrunion/Ornith-1.0-9B-ROCmFPX-GGUF   # or download + add-from-path
hal0 slot create ornith --type llm --hardware rocm --model <id> --ctx-size 32768
curl -sS --max-time 300 -X POST http://127.0.0.1:8080/api/slots/ornith/load

Credits

Base model: deepreinforce-ai — Ornith-1.0-9B (MIT)
BF16 GGUF conversion + imatrix: unsloth
ROCmFPX quant formats + fork: Hal0ai