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

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF
# Run inference directly in the terminal:
llama cli -hf singulared/Ornith-1.0-35B-MTP-ROCmFP4-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 singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF
# Run inference directly in the terminal:
./llama-cli -hf singulared/Ornith-1.0-35B-MTP-ROCmFP4-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 singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF

Use Docker

docker model run hf.co/singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF

LM Studio
Jan

vLLM

How to use singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "singulared/Ornith-1.0-35B-MTP-ROCmFP4-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": "singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF

Ollama
How to use singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF with Ollama:
```
ollama run hf.co/singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF
```

Unsloth Studio

How to use singulared/Ornith-1.0-35B-MTP-ROCmFP4-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 singulared/Ornith-1.0-35B-MTP-ROCmFP4-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 singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF to start chatting

How to use singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF

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": "singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use singulared/Ornith-1.0-35B-MTP-ROCmFP4-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 singulared/Ornith-1.0-35B-MTP-ROCmFP4-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 singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf singulared/Ornith-1.0-35B-MTP-ROCmFP4-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 "singulared/Ornith-1.0-35B-MTP-ROCmFP4-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"

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

Lemonade

How to use singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF

Run and chat with the model

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

List all available models

lemonade list

Ornith-1.0-35B-MTP-ROCmFP4-GGUF / README.md

singulared

Pin exact ROCm 7.15 nightly version (7.15.0a20260721) for reproducibility

46a0084 verified 12 days ago

preview code

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metadata

license: apache-2.0
base_model:
  - deepreinforce-ai/Ornith-1.0-35B
  - Qwen/Qwen3.6-35B-A3B
base_model_relation: merge
pipeline_tag: text-generation
library_name: gguf
tags:
  - gguf
  - rocmfp4
  - strix-halo
  - gfx1151
  - mtp
  - speculative-decoding
  - qwen35moe
  - code
  - agentic

Ornith-1.0-35B-MTP — ROCmFP4 (Strix Halo)

⚠️ Requires the ROCmFPX fork of llama.cpp — this will NOT load on mainline llama.cpp. ROCmFP4 is an experimental AMD FP4 quant format (ggml tensor types 100–107) that only exists in that fork. Targets AMD Strix Halo / Radeon 8060S (gfx1151). For standard GGUF that runs anywhere, use the base repo: → singulared/Ornith-1.0-35B-MTP-GGUF (Q8_0 / Q4_K_M, mainline llama.cpp).

Ornith-1.0-35B (DeepReinforce, a qwen35moe agentic coder) with an embedded MTP (nextn) head grafted in, quantized to ROCmFP4-COHERENT for fast self-speculative decoding on Strix Halo.

What it is / provenance

Body: Q4_0_ROCMFP4_COHERENT (4.70 bpw — ROCmFP4 experts + Q6_K token embeddings), quantized directly from the BF16 original (deepreinforce-ai/Ornith-1.0-35B) — a single clean quantization pass, no intermediate Q8 (avoids double-quant).
MTP head: blk.40.nextn.* grafted raw from Qwen/Qwen3.6-35B-A3B's native head (same qwen35moe arch), after quantization — so eh_proj stays at its original Q8_0. The head's eh_proj precision is the throughput lever, and grafting it raw keeps it high. Ornith is a barely-shifted fine-tune of Qwen3.6-35B-A3B, so the head transfers cleanly (~88% draft acceptance).

Performance (Radeon 8060S / gfx1151, ROCmFPX fork · 19.2 GiB)

Recommended: Vulkan (-dev Vulkan0) — best decode, which dominates a coder's token cost:

decode: 86.7 t/s (MTP n4, -ub 2048) · 72 t/s without MTP · draft acceptance 0.88
prefill: ~1050 t/s (pp4096, -ub 2048)

Backend / ROCm-version comparison (`-ub 2048`)

backend	prefill (pp4096)	decode (MTP `n4`)
Vulkan	1051	86.7
ROCm/HIP @ ROCm 7.2	876	70.5
ROCm/HIP @ ROCm 7.15.0a20260721 nightly (compiled)	1155	68.1

Vulkan wins decode decisively (86.7 vs 70.5) → the serving default (decode dominates a coder's token cost).
Compiling the fork against ROCm 7.15 gives +32% ROCm prefill (876→1155, beating Vulkan's) — but it's the slowest decode, so it only helps prefill-bound work. The gain is compile-time (running a 7.2-built binary on 7.15 libs does nothing). ⚠ ROCm 7.15 is the current TheRock nightly, not a stable release — specifically therock-dist-linux-gfx1151-7.15.0a20260721 (build 2026-07-21), built against the nightly tarball, not a packaged ROCm. Newer nightlies may differ.
Numbers are gfx1151-specific; other Strix Halo parts may differ.

Usage (ROCmFPX fork)

llama-server -m ornith-1.0-35b-MTP-ROCmFP4-COHERENT.gguf \
  -dev Vulkan0 -fa on -ngl 99 -c 131072 --jinja \
  --spec-type draft-mtp --spec-draft-n-max 4 --spec-draft-p-min 0.6 --alias ornith

The MTP head is embedded — no separate draft model. --spec-draft-n-max 4 is the MoE sweet spot (deeper drafts get rejected). Build the fork per its Strix Halo quickstart.

Licensing & attribution

Derivative of two permissively-licensed models; both credited, their licenses apply to their parts:

Quantization: ROCmFP4-COHERENT (ROCmFPX fork). Not affiliated with or endorsed by DeepReinforce, Qwen, or the ROCmFPX author.

Ornith-1.0-35B-MTP — ROCmFP4 (Strix Halo)

What it is / provenance

Performance (Radeon 8060S / gfx1151, ROCmFPX fork · 19.2 GiB)

Backend / ROCm-version comparison (-ub 2048)

Usage (ROCmFPX fork)

Licensing & attribution

Backend / ROCm-version comparison (`-ub 2048`)