Instructions to use AtomicChat/ornith-35b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AtomicChat/ornith-35b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="AtomicChat/ornith-35b-GGUF",
	filename="ornith-35b-Q4_K_M.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 AtomicChat/ornith-35b-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 AtomicChat/ornith-35b-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf AtomicChat/ornith-35b-GGUF:UD-Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AtomicChat/ornith-35b-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf AtomicChat/ornith-35b-GGUF:UD-Q4_K_XL

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 AtomicChat/ornith-35b-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf AtomicChat/ornith-35b-GGUF:UD-Q4_K_XL

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 AtomicChat/ornith-35b-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AtomicChat/ornith-35b-GGUF:UD-Q4_K_XL

Use Docker

docker model run hf.co/AtomicChat/ornith-35b-GGUF:UD-Q4_K_XL

LM Studio
Jan

vLLM

How to use AtomicChat/ornith-35b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AtomicChat/ornith-35b-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": "AtomicChat/ornith-35b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AtomicChat/ornith-35b-GGUF:UD-Q4_K_XL

Ollama
How to use AtomicChat/ornith-35b-GGUF with Ollama:
```
ollama run hf.co/AtomicChat/ornith-35b-GGUF:UD-Q4_K_XL
```

Unsloth Studio

How to use AtomicChat/ornith-35b-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 AtomicChat/ornith-35b-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 AtomicChat/ornith-35b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AtomicChat/ornith-35b-GGUF to start chatting

How to use AtomicChat/ornith-35b-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AtomicChat/ornith-35b-GGUF:UD-Q4_K_XL

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": "AtomicChat/ornith-35b-GGUF:UD-Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use AtomicChat/ornith-35b-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 AtomicChat/ornith-35b-GGUF:UD-Q4_K_XL

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 AtomicChat/ornith-35b-GGUF:UD-Q4_K_XL

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use AtomicChat/ornith-35b-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AtomicChat/ornith-35b-GGUF:UD-Q4_K_XL

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 "AtomicChat/ornith-35b-GGUF:UD-Q4_K_XL" \
  --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 AtomicChat/ornith-35b-GGUF with Docker Model Runner:
```
docker model run hf.co/AtomicChat/ornith-35b-GGUF:UD-Q4_K_XL
```

Lemonade

How to use AtomicChat/ornith-35b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AtomicChat/ornith-35b-GGUF:UD-Q4_K_XL

Run and chat with the model

lemonade run user.ornith-35b-GGUF-UD-Q4_K_XL

List all available models

lemonade list

Base model: deepreinforce-ai/Ornith-1.0-35B

Ornith 1.0 35B, self-quantized to GGUF by Atomic Chat. Built straight from DeepReinforce's original weights with a per-tensor importance matrix, so this is not a repack of somebody else's files. Runs fully offline.

Highlights

0.0B parameters: the weights this repo quantizes.
Context length: 262,144 tokens (256K), as published by DeepReinforce.
40 layers: Mixture-of-Experts.
Modalities: the base model handles Text, Image; this repo ships text-only quants, it carries no vision projector.
Full imatrix ladder: every quant is calibrated with an importance matrix.
State-of-the-Art Coding Agents: Available in 9B-Dense, 31B-Dense, 35B-MoE, and 397B-MoE (post-trained on top of Gemma 4 and Qwen 3.5), achieving state-of-the-art performance among open-source models of comparable size on coding benchmarks such as Terminal-Bench 2.1, SWE-Bench, NL2Repo and OpenClaw.
Self-Improving Training Framework: Ornith-1.0 employs RL to learn to generate not only solution rollouts, but also the scallfold that drive those rollouts. By jointly optimizing the scaffold and the resulting solution, the model discovers better search trajectories and generates higher-quality solutions.

These GGUFs are self-quantized from the original weights, not a repack. The importance matrix keeps low-bit quants closer to the full-precision model.

Always pass --jinja so the Ornith 1.0 35B chat template is applied. Without it the model can emit malformed turns.

Model Overview

Property	Value
Base model	`deepreinforce-ai/Ornith-1.0-35B`
Parameters	0.0B
Layers	40
Experts	256 routed (top-8)
Context length	262,144 tokens (256K)
Vocabulary	248,320
Modalities	Text, Image in the base model; text only in this repo, it ships no vision projector
Architecture	Mixture-of-Experts, 256 experts (top-8), 16 attention heads over 2 KV heads, `Qwen3_5MoeForConditionalGeneration`
This repo	GGUF quants (imatrix). Quants: `Q4_K_M`, `UD-Q4_K_XL`, `Q5_K_M`, `Q6_K`, `Q8_0`

Scores are DeepReinforce's published results for the base deepreinforce-ai/Ornith-1.0-35B, not our own measurements. Quantization preserves the large majority of this; Q4_K_M and up stay close to full precision.

Choosing a quant

Quant	Size	Notes
`Q4_K_M`	21.2 GB	Recommended default. Best balance of size, speed and quality.
`UD-Q4_K_XL`	21.5 GB	Dynamic. Embeddings and output kept at Q8_0 for higher quality at a Q4 footprint.
`Q5_K_M`	24.7 GB	Higher quality, low loss.
`Q6_K`	28.5 GB	Near lossless, noticeably lighter than Q8_0.
`Q8_0`	36.9 GB	Effectively lossless, reference quality.

Pick the largest file that fits your (V)RAM with room for context. Q4_K_M or UD-Q4_K_XL is the sweet spot for most setups; Q6_K or Q8_0 for maximum fidelity.

Get started

Run Ornith 1.0 35B locally with:

Atomic Chat: the easiest path. Open the app, search AtomicChat/ornith-35b-GGUF, pick a quant, hit Use this model.
llama.cpp: llama-server -hf AtomicChat/ornith-35b-GGUF:Q4_K_M --jinja -c 8192
Ollama: ollama run hf.co/AtomicChat/ornith-35b-GGUF:Q4_K_M
LM Studio / Jan: search the repo id, download any quant.

Best practices

Parameter	Value
temperature	1.0
top_p	1.0
top_k	20

DeepReinforce's recommended sampling configuration for deepreinforce-ai/Ornith-1.0-35B.

Run in llama.cpp

git clone https://github.com/ggml-org/llama.cpp
cmake llama.cpp -B llama.cpp/build -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON
cmake --build llama.cpp/build --config Release -j --target llama-cli llama-server

./llama.cpp/build/bin/llama-server \
    -hf AtomicChat/ornith-35b-GGUF:Q4_K_M \
    --jinja -ngl 99 -c 8192 -fa on

How these were made

Download deepreinforce-ai/Ornith-1.0-35B (original weights).
Convert to f16 GGUF with llama.cpp.
Build an importance matrix over our calibration corpus.
Quantize the ladder with --imatrix.
UD-Q4_K_XL additionally pins the token-embedding and output tensors to Q8_0.

License

Original model by DeepReinforce, released under the MIT license. Full terms: MIT. Quantized by Atomic Chat.

Downloads last month: 3,951

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

Model tree for AtomicChat/ornith-35b-GGUF

Base model

deepreinforce-ai/Ornith-1.0-35B

Quantized

(151)

this model

Collection including AtomicChat/ornith-35b-GGUF

Ornith 1.0

Collection

DeepReinforce's Ornith 1.0 agentic-coding models, self-quantized to GGUF by Atomic Chat with a per-tensor importance matrix. Runs fully offline. • 10 items • Updated 22 days ago • 6