Instructions to use dealignai/Ornith-1.5-35B-A3B-UNCENSORED-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 dealignai/Ornith-1.5-35B-A3B-UNCENSORED-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 dealignai/Ornith-1.5-35B-A3B-UNCENSORED-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf dealignai/Ornith-1.5-35B-A3B-UNCENSORED-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 dealignai/Ornith-1.5-35B-A3B-UNCENSORED-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf dealignai/Ornith-1.5-35B-A3B-UNCENSORED-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 dealignai/Ornith-1.5-35B-A3B-UNCENSORED-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf dealignai/Ornith-1.5-35B-A3B-UNCENSORED-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 dealignai/Ornith-1.5-35B-A3B-UNCENSORED-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dealignai/Ornith-1.5-35B-A3B-UNCENSORED-GGUF:Q4_K_M

Use Docker

docker model run hf.co/dealignai/Ornith-1.5-35B-A3B-UNCENSORED-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use dealignai/Ornith-1.5-35B-A3B-UNCENSORED-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dealignai/Ornith-1.5-35B-A3B-UNCENSORED-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": "dealignai/Ornith-1.5-35B-A3B-UNCENSORED-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/dealignai/Ornith-1.5-35B-A3B-UNCENSORED-GGUF:Q4_K_M

Ollama
How to use dealignai/Ornith-1.5-35B-A3B-UNCENSORED-GGUF with Ollama:
```
ollama run hf.co/dealignai/Ornith-1.5-35B-A3B-UNCENSORED-GGUF:Q4_K_M
```

Unsloth Studio

How to use dealignai/Ornith-1.5-35B-A3B-UNCENSORED-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 dealignai/Ornith-1.5-35B-A3B-UNCENSORED-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 dealignai/Ornith-1.5-35B-A3B-UNCENSORED-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for dealignai/Ornith-1.5-35B-A3B-UNCENSORED-GGUF to start chatting

How to use dealignai/Ornith-1.5-35B-A3B-UNCENSORED-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf dealignai/Ornith-1.5-35B-A3B-UNCENSORED-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "dealignai/Ornith-1.5-35B-A3B-UNCENSORED-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use dealignai/Ornith-1.5-35B-A3B-UNCENSORED-GGUF with Docker Model Runner:
```
docker model run hf.co/dealignai/Ornith-1.5-35B-A3B-UNCENSORED-GGUF:Q4_K_M
```

Lemonade

How to use dealignai/Ornith-1.5-35B-A3B-UNCENSORED-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dealignai/Ornith-1.5-35B-A3B-UNCENSORED-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Ornith-1.5-35B-A3B-UNCENSORED-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use dealignai/Ornith-1.5-35B-A3B-UNCENSORED-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 dealignai/Ornith-1.5-35B-A3B-UNCENSORED-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 dealignai/Ornith-1.5-35B-A3B-UNCENSORED-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use dealignai/Ornith-1.5-35B-A3B-UNCENSORED-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf dealignai/Ornith-1.5-35B-A3B-UNCENSORED-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 "dealignai/Ornith-1.5-35B-A3B-UNCENSORED-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"

Dealign.ai

Ornith-1.5-35B-A3B-CRACK-GGUF

CRACK-abliterated Ornith 1.5 35B-A3B (MoE) — GGUF quants for llama.cpp. Four quantizations (Q8_0 / Q6_K / Q4_K_M / Q2_K) in one repository. Refusal behavior removed while preserving knowledge, reasoning ("thinking"), the MTP speculative head, and full Vision-Language capability.

Ornith 1.5 35B-A3B is a hybrid GatedDeltaNet (SSM) + attention Mixture-of-Experts (256 experts, 8 active) with a multi-token-prediction head. CRACK uses architecture-aware weight surgery targeting the attention pathways, so knowledge and coherence are fully retained (MMLU within noise of base at every quant).

Research artifact with reduced safety guardrails. Use responsibly and lawfully.

Quantizations

File	Size	Notes
`Ornith-1.5-35B-A3B-CRACK-Q8_0.gguf`	37.8 GB	near-lossless reference
`Ornith-1.5-35B-A3B-CRACK-Q6_K.gguf`	29.2 GB	near-lossless
`Ornith-1.5-35B-A3B-CRACK-Q5_K_M.gguf`	25 GB	high quality
`Ornith-1.5-35B-A3B-CRACK-Q4_K_M.gguf`	21.7 GB	balanced (recommended)
`Ornith-1.5-35B-A3B-CRACK-Q3_K_M.gguf`	17 GB	small
`Ornith-1.5-35B-A3B-CRACK-Q2_K.gguf`	13.2 GB	smallest

Pick one text file plus the vision projector mmproj-Ornith-1.5-35B-A3B-f16.gguf for image input. Each quant is independently tuned (its own surgery strength) and verified. Sub-8-bit quants use an AWQ (activation-aware / Hessian) pass plus an importance matrix for maximum quality. The MTP speculative head is preserved and abliterated in all quants.

Benchmarks

MMLU is logit-mode accuracy (base vs. CRACK at the same quant — isolates knowledge retention). HarmBench is coherence-gated attack-success-rate over the 240 standard/ contextual harm behaviors (copyright behaviors excluded from the safety gate).

Quant	MMLU (base)	MMLU (CRACK)	ΔMMLU	HarmBench harm-ASR
`Q8_0`	79.0%	80.7%	+1.75 pp	100.0%
`Q6_K`	78.6%	79.0%	+0.40 pp	100.0%
`Q5_K_M`	80.7%	80.1%	-0.58 pp	100.0%
`Q4_K_M`	80.0%	77.8%	-2.20 pp	100.0%
`Q3_K_M`	76.0%	78.4%	+2.34 pp	100.0%
`Q2_K`	77.9%	69.8%	-8.07 pp	99.6%

MMLU is retained within noise of base at every quant (abliteration even improves it at Q8 — removing refusal circuitry reduces "parasitic" activation noise).

Q2_K note: at 2-bit, abliteration surgery interacts with the aggressive quantization, so Q2_K carries a larger MMLU cost than the higher quants (the MoE base quantizes to 2-bit unusually well). Compliance stays at 99.6%. For best quality use Q4_K_M or higher.

HarmBench harm-ASR by topic (CRACK)

Topic	harm-ASR
chemical / biological	100.0%
cybercrime / intrusion	100.0%
harassment / bullying	100.0%
harmful	100.0%
illegal	100.0%
misinformation / disinformation	100.0%

Usage (llama.cpp)

llama-cli -m Ornith-1.5-35B-A3B-CRACK-Q4_K_M.gguf -cnv --jinja \
  --temp 1.0 --top-p 0.95 --top-k 20
# or serve:
llama-server -m Ornith-1.5-35B-A3B-CRACK-Q4_K_M.gguf --jinja \
  --temp 1.0 --top-p 0.95 --top-k 20 -c 8192

Recommended sampling: temperature=1.0, top_p=0.95, top_k=20.

Reasoning

Ornith 1.5 emits a <think> reasoning trace and it is ON by default. Disable with {"chat_template_kwargs": {"enable_thinking": false}}. Works out of the box in LM Studio.

Vision (image + text)

Download a text quant and mmproj-Ornith-1.5-35B-A3B-f16.gguf:

llama-mtmd-cli -m Ornith-1.5-35B-A3B-CRACK-Q4_K_M.gguf \
  --mmproj mmproj-Ornith-1.5-35B-A3B-f16.gguf --jinja \
  --image photo.jpg -p "Describe this image."

The same mmproj works with all four text quants.

License

MIT (inherited from the upstream Ornith 1.5 base model).

Contact

eric@dealign.ai

Downloads last month: 10,239

GGUF

Model size

36B params

Architecture

qwen35moe

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for dealignai/Ornith-1.5-35B-A3B-UNCENSORED-GGUF

Base model

ornith-ai/Ornith-1.5-35B-A3B

Quantized

(87)

this model