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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	---
	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](https://github.com/charlie12345/ROCmFPX) 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](https://huggingface.co/singulared/Ornith-1.0-35B-MTP-GGUF) (Q8_0 / Q4_K_M, mainline llama.cpp).

	[Ornith-1.0-35B](https://huggingface.co/deepreinforce-ai/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`](https://huggingface.co/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](https://github.com/ROCm/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)

	```bash
	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:
	- Ornith-1.0-35B — © DeepReinforce — MIT — the base weights.
	- Qwen3.6-35B-A3B — © Alibaba Cloud / Qwen — Apache-2.0 — the grafted MTP head.

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