Instructions to use gsrunion/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN-DFLASH-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-ROCmFP4-STRIX_LEAN-DFLASH-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-ROCmFP4-STRIX_LEAN-DFLASH-GGUF
# Run inference directly in the terminal:
llama cli -hf gsrunion/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN-DFLASH-GGUF

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-ROCmFP4-STRIX_LEAN-DFLASH-GGUF
# Run inference directly in the terminal:
llama cli -hf gsrunion/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN-DFLASH-GGUF

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-ROCmFP4-STRIX_LEAN-DFLASH-GGUF
# Run inference directly in the terminal:
./llama-cli -hf gsrunion/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN-DFLASH-GGUF

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-ROCmFP4-STRIX_LEAN-DFLASH-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf gsrunion/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN-DFLASH-GGUF

Use Docker

docker model run hf.co/gsrunion/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN-DFLASH-GGUF

LM Studio
Jan

vLLM

How to use gsrunion/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN-DFLASH-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-ROCmFP4-STRIX_LEAN-DFLASH-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-ROCmFP4-STRIX_LEAN-DFLASH-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/gsrunion/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN-DFLASH-GGUF

Ollama
How to use gsrunion/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN-DFLASH-GGUF with Ollama:
```
ollama run hf.co/gsrunion/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN-DFLASH-GGUF
```
Unsloth Desktop

How to use gsrunion/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN-DFLASH-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-ROCmFP4-STRIX_LEAN-DFLASH-GGUF

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": "gsrunion/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN-DFLASH-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use gsrunion/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN-DFLASH-GGUF with Docker Model Runner:
```
docker model run hf.co/gsrunion/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN-DFLASH-GGUF
```

Lemonade

How to use gsrunion/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN-DFLASH-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull gsrunion/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN-DFLASH-GGUF

Run and chat with the model

lemonade run user.Ornith-1.0-35B-ROCmFP4-STRIX_LEAN-DFLASH-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use gsrunion/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN-DFLASH-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-ROCmFP4-STRIX_LEAN-DFLASH-GGUF

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-ROCmFP4-STRIX_LEAN-DFLASH-GGUF

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use gsrunion/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN-DFLASH-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-ROCmFP4-STRIX_LEAN-DFLASH-GGUF

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-ROCmFP4-STRIX_LEAN-DFLASH-GGUF" \
  --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 — ROCmFP4 STRIX_LEAN, DFlash baked in (cross-model draft)

A single-file, self-accelerating GGUF. Notably: the embedded DFlash draft was trained for a different model — Qwen3.6-35B-A3B — and still runs at 98.9% acceptance here, because Ornith shares its architectural lineage (Qwen3.5-MoE) with the draft's intended target. See z-lab/dflash#149 for the write-up on this transfer finding.

No --model-draft, no --spec-type flag — point -m at this file and speculative decoding just happens.

llama-server -m Ornith-1.0-35B-STRIX_LEAN-DFLASH.gguf -ngl 999 -fa on --jinja -c 65536

Requirements

Needs a ROCmFP4-aware build and DFlash-graft support for embedded drafts:

gsrunion/rocmfp4-llama branch dflash-graft, validated on AMD Strix Halo / gfx1151

On first load the server extracts the draft to a small cached sidecar next to the model (~1 second, one-time).

Measured performance

AMD Ryzen AI Max+ 395 (Strix Halo, 128 GB unified LPDDR5X), server-timing, self-accelerating load:

	tok/s	acceptance
Baked single-file, cross-model draft	111.6	98.9% (430/435)
Two-file cross-model config	112.5	97–98%
Plain LEAN, no draft	63.1	—

+77% over plain decode, entirely from a draft model that was never trained on Ornith.

Credits

Base model: deepreinforce-ai — Ornith-1.0-35B (MIT)
DFlash draft (cross-model): z-lab — trained for Qwen3.6-35B-A3B
ROCmFP4 quant formats: Hal0ai; fork base: charlie12345
Vision (mmproj) not included — DFlash and mmproj cannot currently coexist in this serving stack; use the base ROCmFPX release for multimodal.

Downloads last month: 460

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for gsrunion/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN-DFLASH-GGUF

Base model

ornith-ai/Ornith-1.0-35B

Quantized

(178)

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