Instructions to use sphaela/Ornith-1.0-9B-AutoRound-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sphaela/Ornith-1.0-9B-AutoRound-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sphaela/Ornith-1.0-9B-AutoRound-GGUF",
	filename="Ornith-1.0-9B-Q2_K_S.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 sphaela/Ornith-1.0-9B-AutoRound-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 sphaela/Ornith-1.0-9B-AutoRound-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf sphaela/Ornith-1.0-9B-AutoRound-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf sphaela/Ornith-1.0-9B-AutoRound-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf sphaela/Ornith-1.0-9B-AutoRound-GGUF:Q4_K_M

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 sphaela/Ornith-1.0-9B-AutoRound-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf sphaela/Ornith-1.0-9B-AutoRound-GGUF:Q4_K_M

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 sphaela/Ornith-1.0-9B-AutoRound-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sphaela/Ornith-1.0-9B-AutoRound-GGUF:Q4_K_M

Use Docker

docker model run hf.co/sphaela/Ornith-1.0-9B-AutoRound-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use sphaela/Ornith-1.0-9B-AutoRound-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sphaela/Ornith-1.0-9B-AutoRound-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": "sphaela/Ornith-1.0-9B-AutoRound-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sphaela/Ornith-1.0-9B-AutoRound-GGUF:Q4_K_M

Ollama
How to use sphaela/Ornith-1.0-9B-AutoRound-GGUF with Ollama:
```
ollama run hf.co/sphaela/Ornith-1.0-9B-AutoRound-GGUF:Q4_K_M
```

Unsloth Studio

How to use sphaela/Ornith-1.0-9B-AutoRound-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 sphaela/Ornith-1.0-9B-AutoRound-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 sphaela/Ornith-1.0-9B-AutoRound-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sphaela/Ornith-1.0-9B-AutoRound-GGUF to start chatting

How to use sphaela/Ornith-1.0-9B-AutoRound-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sphaela/Ornith-1.0-9B-AutoRound-GGUF:Q4_K_M

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": "sphaela/Ornith-1.0-9B-AutoRound-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use sphaela/Ornith-1.0-9B-AutoRound-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 sphaela/Ornith-1.0-9B-AutoRound-GGUF:Q4_K_M

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 sphaela/Ornith-1.0-9B-AutoRound-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use sphaela/Ornith-1.0-9B-AutoRound-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sphaela/Ornith-1.0-9B-AutoRound-GGUF:Q4_K_M

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 "sphaela/Ornith-1.0-9B-AutoRound-GGUF:Q4_K_M" \
  --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 sphaela/Ornith-1.0-9B-AutoRound-GGUF with Docker Model Runner:
```
docker model run hf.co/sphaela/Ornith-1.0-9B-AutoRound-GGUF:Q4_K_M
```

Lemonade

How to use sphaela/Ornith-1.0-9B-AutoRound-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sphaela/Ornith-1.0-9B-AutoRound-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Ornith-1.0-9B-AutoRound-GGUF-Q4_K_M

List all available models

lemonade list

Ornith-1.0-9B AutoRound GGUF

GGUF quantizations of deepreinforce-ai/Ornith-1.0-9B, produced with Intel AutoRound using its alg_ext iterative calibration (~200 optimization steps per block) rather than plain round-to-nearest. At a given size this recovers noticeably more quality than vanilla GGUF quants, with the largest benefit at the low bit widths.

Ornith-1.0 is a self-improving, MIT-licensed family of open-source agentic-coding models from Deep Reinforce; the 9B is the lightweight, single-GPU member (dense, 32 layers, 256K context). See the original model card for capabilities and coding benchmarks. This repo only re-packages those weights as GGUF.

Quality vs. size

Every quant was measured against the BF16 reference on wikitext-2 test: PPL and, more importantly, KL-divergence of the output distribution (how far the quant drifts from full precision; lower is better). Top-1 is the share of positions whose argmax token still matches BF16. Reference BF16 PPL = 8.13.

Scheme	Size	bpw	PPL	ΔPPL	Mean KLD	Top-1 agree
Q2_K_S	3.7 GB	3.30	9.89	+21.7%	0.2960	76.8%
Q3_K_S	4.3 GB	3.81	8.53	+4.9%	0.1216	85.0%
Q3_K_M	4.5 GB	4.04	8.57	+5.4%	0.1214	84.9%
Q3_K_L	4.9 GB	4.40	8.60	+5.8%	0.1201	84.9%
Q4_K_S	5.4 GB	4.78	8.05	-1.0%	0.0425	91.0%
Q4_K_M ⭐	5.6 GB	5.03	8.08	-0.6%	0.0406	91.2%
Q6_K	7.4 GB	6.58	8.15	+0.3%	0.0040	96.6%
Q8_0	9.5 GB	8.51	8.14	+0.1%	0.0020	97.5%

Recommendations

Q4_K_M ⭐ is the best all-round pick: smaller than the Q5 tier yet closest of the mid-size quants to BF16 (KLD 0.04, Top-1 91%). Start here.
Q6_K / Q8_0 are effectively lossless (KLD under 0.004) when you have the VRAM.
Q3_K_* are usable on tight memory. Reach for Q2_K_S only when you must, since 2-bit shows noticeable drift.

Why no Q5 or Q2_K_MIXED? On the KLD benchmark the Q5_K quants were not closer to BF16 than Q4_K_M despite being larger, so they add size without quality for this model and were omitted. The Q2_K_MIXED variant was defective here (broken output distribution) and was also excluded. (This base checkpoint ships no MTP/NextN head, so there is no speculative-decoding block to preserve.)

Multimodal

Ornith inherits a vision tower. Multimodal projectors are included for use via llama-mtmd-cli (pair any LM quant above with one of these). For text-only / coding use you can ignore them.

Projector	Size	Notes
`mmproj-Ornith-1.0-9B-f16.gguf`	0.9 GB	recommended (half size, no visible quality loss)
`mmproj-Ornith-1.0-9B-bf16.gguf`	0.9 GB	bf16 alternative
`mmproj-Ornith-1.0-9B-f32.gguf`	1.8 GB	full precision

Usage (llama.cpp)

# chat / completion (the model's chat template, incl. <think> reasoning, is embedded)
llama-cli   -m Ornith-1.0-9B-Q4_K_M.gguf -ngl 999 -c 8192 -p "Write a Python LRU cache."

# OpenAI-compatible server
llama-server -m Ornith-1.0-9B-Q4_K_M.gguf -ngl 999 -c 8192

# multimodal (image + text)
llama-mtmd-cli -m Ornith-1.0-9B-Q4_K_M.gguf \
               --mmproj mmproj-Ornith-1.0-9B-f32.gguf \
               --image picture.jpg -p "Describe this screenshot."

How these were made

auto-round --model deepreinforce-ai/Ornith-1.0-9B \
           --scheme gguf:q4_k_m --enable_alg_ext --enable_torch_compile \
           --output_dir ./quantized

All outputs were NaN-scanned and inference-checked. The comparison table and the Pareto plot above (pareto.png, comparison.csv in this repo) come from llama-perplexity --kl-divergence against the BF16 reference.

Support

These quantized models are made in my spare time using expensive hardware such as DGX Spark systems for quantization and validation. If you find these GGUFs useful for your projects, consider buying me a coffee to help cover hardware and compute costs. Every bit of support helps me keep producing high-quality quantized models for the community!

☕ Support me on Ko-fi

Credits & license

Quantization: Intel AutoRound (Apache-2.0) and llama.cpp.

Downloads last month: 815

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

2-bit

3-bit

4-bit

6-bit

8-bit

Model tree for sphaela/Ornith-1.0-9B-AutoRound-GGUF

Base model

deepreinforce-ai/Ornith-1.0-9B

Quantized

(85)

this model