Instructions to use SandLogicTechnologies/Ornith-1.0-9B-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 SandLogicTechnologies/Ornith-1.0-9B-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 SandLogicTechnologies/Ornith-1.0-9B-GGUF:IQ3_M
# Run inference directly in the terminal:
llama cli -hf SandLogicTechnologies/Ornith-1.0-9B-GGUF:IQ3_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SandLogicTechnologies/Ornith-1.0-9B-GGUF:IQ3_M
# Run inference directly in the terminal:
llama cli -hf SandLogicTechnologies/Ornith-1.0-9B-GGUF:IQ3_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 SandLogicTechnologies/Ornith-1.0-9B-GGUF:IQ3_M
# Run inference directly in the terminal:
./llama-cli -hf SandLogicTechnologies/Ornith-1.0-9B-GGUF:IQ3_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 SandLogicTechnologies/Ornith-1.0-9B-GGUF:IQ3_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SandLogicTechnologies/Ornith-1.0-9B-GGUF:IQ3_M

Use Docker

docker model run hf.co/SandLogicTechnologies/Ornith-1.0-9B-GGUF:IQ3_M

LM Studio
Jan

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SandLogicTechnologies/Ornith-1.0-9B-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": "SandLogicTechnologies/Ornith-1.0-9B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/SandLogicTechnologies/Ornith-1.0-9B-GGUF:IQ3_M

Ollama
How to use SandLogicTechnologies/Ornith-1.0-9B-GGUF with Ollama:
```
ollama run hf.co/SandLogicTechnologies/Ornith-1.0-9B-GGUF:IQ3_M
```

Unsloth Studio

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

Using HuggingFace Spaces for Unsloth

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

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

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SandLogicTechnologies/Ornith-1.0-9B-GGUF:IQ3_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": "SandLogicTechnologies/Ornith-1.0-9B-GGUF:IQ3_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

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

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SandLogicTechnologies/Ornith-1.0-9B-GGUF:IQ3_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 "SandLogicTechnologies/Ornith-1.0-9B-GGUF:IQ3_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 SandLogicTechnologies/Ornith-1.0-9B-GGUF with Docker Model Runner:
```
docker model run hf.co/SandLogicTechnologies/Ornith-1.0-9B-GGUF:IQ3_M
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SandLogicTechnologies/Ornith-1.0-9B-GGUF:IQ3_M

Run and chat with the model

lemonade run user.Ornith-1.0-9B-GGUF-IQ3_M

List all available models

lemonade list

Hermes Agent

How to use SandLogicTechnologies/Ornith-1.0-9B-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 SandLogicTechnologies/Ornith-1.0-9B-GGUF:IQ3_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 SandLogicTechnologies/Ornith-1.0-9B-GGUF:IQ3_M

Run Hermes

hermes

Atomic Chat

Ornith-1.0-9B

Ornith-1.0-9B is a dense 9B-parameter reasoning model developed by the Ornith/DeepReinforce team as part of the Ornith-1.0 family of open-source models specialized for agentic coding. It is designed to operate in multi-step software engineering workflows, combining reasoning, tool calling, code generation, and iterative problem solving.

This repository contains GGUF quantized variants of Ornith-1.0-9B optimized for efficient local inference. The quantized formats substantially reduce the memory footprint of the model while retaining its core reasoning and agentic coding capabilities.

Ornith-1.0-9B is a reasoning model that produces an explicit reasoning phase before its final response and supports structured tool calls when deployed through compatible serving frameworks. The official model card reports strong results on agentic coding benchmarks including SWE-bench Verified, SWE-bench Pro, Terminal-Bench 2.1, NL2Repo, and ClawEval.

Model Overview

Model Name: Ornith-1.0-9B
Base Model: ornith-ai/Ornith-1.0-9B
Architecture: Dense language model
Parameter Count: Approximately 9 Billion Parameters
Modalities: Text, Image
Model Type: Reasoning / Agentic Coding Model
Context Length: Up to 262,144 tokens in the official serving configurations
Developer: Ornith / DeepReinforce Team
License: MIT

Quantization Formats

This repository provides various GGUF quantized versions of the Ornith-1.0-9B model optimized for efficient local inference.

IQ3_M

Size reduction of approx 75.37% (4.11 GB)compared to 16-bit (16.69 GB)
Aggressive 3-bit quantization designed to minimize memory requirements for agentic reasoning workloads
Suitable for local coding agents, developer tools, and constrained inference environments
Enables deployment of a 9B reasoning model on substantially smaller hardware configurations
Complex software-engineering reasoning, long-context interactions, and multi-step tool workflows may experience greater quality degradation than higher-precision variants

IQ4_NL

Size reduction of approx 69.62% (5.07 GB)compared to 16-bit (16.69 GB)
Advanced 4-bit non-linear quantization designed to retain reasoning and code-generation fidelity
Better suited for agentic coding, debugging, repository-level analysis, and tool-assisted workflows
Provides a stronger balance between model capability and memory consumption than the more aggressive IQ3_M format
May introduce additional computational overhead during inference compared with lower-bit quantization

IQ4_XS

Size reduction of approx 70.82% (4.87 GB)compared to 16-bit (16.69 GB)
Balanced 4-bit quantization focused on maintaining reasoning stability while reducing deployment requirements
Suitable for local coding agents, software-engineering assistants, and extended problem-solving sessions
Provides a practical compromise between memory footprint, generation quality, and inference efficiency
Maintains dependable behavior across most practical agentic coding workloads

Q6_K

Size reduction of approx 58.96% (6.85 GB)compared to 16-bit (16.69 GB)
Higher-precision 6-bit K-Quant format intended to preserve more of the original model's reasoning and generation behavior
Better suited for demanding coding-agent workflows, repository analysis, complex debugging, and multi-step problem solving
Provides greater headroom for retaining model fidelity than the lower-bit variants
Recommended when reasoning quality and coding reliability are more important than minimizing memory consumption

Training Background (Original Model)

Ornith-1.0 is described by its developers as a self-improving family of open-source models specialized for agentic coding. The training approach uses reinforcement learning to optimize not only solution rollouts but also the scaffolds that drive those rollouts. This allows the model to learn search trajectories for solving complex software-engineering tasks.

Agentic Training

Reinforcement-learning-based training focused on agentic software-engineering workflows
Joint optimization of solution rollouts and the scaffolds used to produce those solutions
Training designed to improve search trajectories and multi-step problem solving
Post-training performed on top of model families including Gemma 4 and Qwen 3.5 within the broader Ornith-1.0 family

Reasoning and Tool Use

Generates a reasoning block before the final answer
Supports structured tool calls through compatible serving runtimes
Designed to interact with coding environments and external tools
Optimized for terminal-oriented and repository-level software engineering workflows

Key Capabilities

Agentic Coding Designed to solve software-engineering tasks through iterative reasoning, tool interaction, and execution-oriented workflows.
Code Generation & Modification Generates and modifies code as part of larger software-development tasks rather than limiting itself to isolated code snippets.
Repository-Level Reasoning Supports analysis of larger software projects and multi-file development problems.
Tool Calling Produces structured function calls that can be surfaced through OpenAI-compatible serving APIs.
Reasoning Uses an explicit reasoning stage to work through complex multi-step problems before producing the final response.
Long-Context Processing The official serving configurations support contexts up to 262,144 tokens, enabling analysis of large codebases and extended agent interactions.
Multimodal Input The Hugging Face model is exposed with image-text-to-text support, allowing visual information to be incorporated alongside textual prompts.

Recommended Usecases

Agentic Coding Systems Build coding agents capable of reasoning through software tasks and interacting with development tools.
Repository-Level Software Engineering Analyze and modify larger codebases involving multiple files and dependencies.
Automated Debugging Investigate software problems through iterative reasoning and tool-assisted workflows.
Terminal-Based Coding Agents Integrate the model into command-line development environments and coding-agent frameworks.
Developer Productivity Automate repetitive software-engineering tasks, code changes, and repository operations.
Tool-Calling Applications Connect the model to external functions, APIs, shell tools, or agent frameworks.
Research & Experimentation Evaluate reinforcement-learning-based agentic coding models and local reasoning systems.

Usage Example

Using llama.cpp

./llama-server \
  -hf SandLogicTechnologies/Ornith-1.0-9B-GGUF \
  --port 8000 \
  -c 262144

For the GGUF files in this repository, select the desired quantization according to available memory and required reasoning fidelity.

Acknowledgments

These quantized models are based on the original work of the Ornith / DeepReinforce team.

Special thanks to:

The Ornith / DeepReinforce team for developing and releasing Ornith-1.0-9B.

The developers and open-source community behind llama.cpp for enabling efficient GGUF-based inference.

Contact

For questions, feedback, or support, please reach out at support@sandlogic.com or visit https://www.sandlogic.com/

Downloads last month: 94

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

3-bit

4-bit

6-bit

Model tree for SandLogicTechnologies/Ornith-1.0-9B-GGUF

Base model

ornith-ai/Ornith-1.0-9B

Quantized

(101)

this model