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

Libraries

How to use singulared/Ornith-1.0-35B-MTP-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="singulared/Ornith-1.0-35B-MTP-GGUF",
	filename="ornith-1.0-35b-MTP-Q8_0.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use singulared/Ornith-1.0-35B-MTP-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-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf singulared/Ornith-1.0-35B-MTP-GGUF:Q8_0

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-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf singulared/Ornith-1.0-35B-MTP-GGUF:Q8_0

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-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf singulared/Ornith-1.0-35B-MTP-GGUF:Q8_0

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-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf singulared/Ornith-1.0-35B-MTP-GGUF:Q8_0

Use Docker

docker model run hf.co/singulared/Ornith-1.0-35B-MTP-GGUF:Q8_0

LM Studio
Jan

vLLM

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

Use Docker

docker model run hf.co/singulared/Ornith-1.0-35B-MTP-GGUF:Q8_0

Ollama
How to use singulared/Ornith-1.0-35B-MTP-GGUF with Ollama:
```
ollama run hf.co/singulared/Ornith-1.0-35B-MTP-GGUF:Q8_0
```

Unsloth Studio

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

How to use singulared/Ornith-1.0-35B-MTP-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-GGUF:Q8_0

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-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use singulared/Ornith-1.0-35B-MTP-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-GGUF:Q8_0

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-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use singulared/Ornith-1.0-35B-MTP-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-GGUF:Q8_0

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-GGUF:Q8_0" \
  --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-GGUF with Docker Model Runner:
```
docker model run hf.co/singulared/Ornith-1.0-35B-MTP-GGUF:Q8_0
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull singulared/Ornith-1.0-35B-MTP-GGUF:Q8_0

Run and chat with the model

lemonade run user.Ornith-1.0-35B-MTP-GGUF-Q8_0

List all available models

lemonade list

Ornith-1.0-35B-MTP (GGUF, Q8_0)

Ornith-1.0-35B (DeepReinforce) with an embedded MTP (Multi-Token Prediction / nextn) head grafted in, enabling self-speculative decoding in llama.cpp for ~+30% decode throughput at identical output quality.

Why

Ornith-1.0-35B is an agentic coder fine-tuned from Qwen3.6-35B-A3B (same qwen35moe architecture, same tokenizer). The base Qwen ships with an embedded MTP head; Ornith's release does not — so it decodes without self-speculation (~50 t/s). Because the fine-tune barely shifts the relevant hidden states, the base model's MTP head transfers almost perfectly when grafted in.

Measured (llama.cpp Vulkan, Radeon 8060S / Strix Halo)

	decode t/s	draft acceptance
Ornith-1.0-35B Q8_0 (no MTP)	~50	—
this model (Q8_0 + grafted MTP)	63–66	0.859 (mean accepted draft len 3.05 / 3)

The weights are unchanged → output quality is identical; the speedup is pure self-speculation.

Usage (llama.cpp)

llama-server -m ornith-1.0-35b-MTP-Q8_0.gguf \
  --spec-type draft-mtp --spec-draft-n-max 3 --spec-draft-p-min 0.6 \
  -fa on -ngl 99 -c 131072 --jinja --alias ornith

The MTP head is embedded — no separate draft model (-md) required.

How it was made (reproducible)

The donor MTP block (blk.40 = a full nextn layer: attention + MoE experts + nextn.{eh_proj, enorm, hnorm, shared_head_norm}) was spliced from Qwen3.6-35B-A3B-Q8_0.gguf into Ornith-1.0-35B-Q8_0.gguf with gguf-py:

Copy all of Ornith's tensors + metadata (raw quantized round-trip — no dequant/requant).
Append Qwen's 20 blk.40.* tensors.
Set qwen35moe.block_count = 41 and add qwen35moe.nextn_predict_layers = 1.

Both bases share the architecture + tokenizer, so the head plugs in directly with no retraining.

Licensing & attribution

A derivative of two permissively-licensed models; both are credited and their licenses apply to their respective parts:

Quantization: Q8_0. Not affiliated with or endorsed by DeepReinforce or the Qwen team.

Downloads last month: 464

GGUF

Model size

36B params

Architecture

qwen35moe

Hardware compatibility

8-bit

Model tree for singulared/Ornith-1.0-35B-MTP-GGUF

Qwen/Qwen3.6-35B-A3B

deepreinforce-ai/Ornith-1.0-35B

Merge model

this model