Instructions to use 0xKitkat/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 0xKitkat/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 0xKitkat/Ornith-1.5-35B-A3B-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf 0xKitkat/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 0xKitkat/Ornith-1.5-35B-A3B-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf 0xKitkat/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 0xKitkat/Ornith-1.5-35B-A3B-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf 0xKitkat/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 0xKitkat/Ornith-1.5-35B-A3B-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf 0xKitkat/Ornith-1.5-35B-A3B-Uncensored-GGUF:Q4_K_M

Use Docker

docker model run hf.co/0xKitkat/Ornith-1.5-35B-A3B-Uncensored-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use 0xKitkat/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 "0xKitkat/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": "0xKitkat/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/0xKitkat/Ornith-1.5-35B-A3B-Uncensored-GGUF:Q4_K_M

Ollama
How to use 0xKitkat/Ornith-1.5-35B-A3B-Uncensored-GGUF with Ollama:
```
ollama run hf.co/0xKitkat/Ornith-1.5-35B-A3B-Uncensored-GGUF:Q4_K_M
```
Unsloth Desktop

How to use 0xKitkat/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 0xKitkat/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": "0xKitkat/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 0xKitkat/Ornith-1.5-35B-A3B-Uncensored-GGUF with Docker Model Runner:
```
docker model run hf.co/0xKitkat/Ornith-1.5-35B-A3B-Uncensored-GGUF:Q4_K_M
```

Lemonade

How to use 0xKitkat/Ornith-1.5-35B-A3B-Uncensored-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull 0xKitkat/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 0xKitkat/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 0xKitkat/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 0xKitkat/Ornith-1.5-35B-A3B-Uncensored-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use 0xKitkat/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 0xKitkat/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 "0xKitkat/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"

Ornith 1.5 35B-A3B Uncensored-GGUF

Follow @procrastiness on Twitter for new model releases and updates.

An uncensored derivative of ornith-ai/Ornith-1.5-35B-A3B, preserving Ornith's coding/agentic post-training, native vision tower, 262K context configuration, and native multi-token-prediction (MTP) head.

Method

This is a streamed task-vector transplant, not a prompt wrapper:

output = Ornith-1.5 + 1.0 * (Qwen3.6-Abliterated - Qwen3.6-Base)

The donor delta comes from wangzhang/Qwen3.6-35B-A3B-abliterated, whose card documents rank-1 attention/MLP steering, expert-granular abliteration, router suppression, orthogonalization, and Gaussian layer decay. Applying its low-KL Qwen task vector to Ornith is intended to transfer refusal suppression without replacing Ornith's self-improvement RL and coding specialization.

Only exact name-and-shape-compatible tensors were eligible:

Target tensors: 1,811
Compatible tensors: 693
Modified tensors: 102
Unchanged compatible tensors: 591
Ornith-only tensors preserved: 1,118

All arithmetic was performed in float32 and rounded once to the target BF16 dtype. Target-only vision/MTP tensors were copied unchanged.

Reproducible source revisions

Ornith: e4dfb35a93d4b6822a811a7676f3488514abe7e2
Qwen base: 995ad96eacd98c81ed38be0c5b274b04031597b0
Abliterated donor: 13db4501cbaf158956f470a990101500ad825f64
Task-vector strength: 1.0

The machine-readable task_vector_report.json contains per-shard SHA-256 hashes and the 100 tensors with the largest relative deltas.

Validation

Checkpoint valid: True
Safetensors shards: 16
Tensors scanned: 1,811
Weight bytes scanned: 71,903,645,408
NaN/Inf scan: all floating-point tensors
Build tests: streamed merge formula and target-only preservation

Files

File	Format	Size
`mmproj-Ornith-1.5-35B-Uncensored-BF16.gguf`	Vision projector	0.84 GiB
`Ornith-1.5-35B-Uncensored-BF16.gguf`	BF16	66.19 GiB
`Ornith-1.5-35B-Uncensored-Q4_K_M.gguf`	Q4_K_M	20.22 GiB
`Ornith-1.5-35B-Uncensored-Q5_K_M.gguf`	Q5_K_M	23.61 GiB

Local smoke evaluation

The release was tested through llama.cpp on the Q4_K_M build. Full details and raw generations are included in evaluation_report.json.

Label: Ornith-1.5-35B-Uncensored-Q4_K_M
Prompts: 20
Refusal Prompts: 16
Heuristic Refusals: 0
Heuristic No Refusal Rate: 1.0
Capability Prompts: 4
Capability Passes: 4
Method: public prompts; deterministic generation; disclosed regex screen

Usage

llama-server -hf 0xKitkat/Ornith-1.5-35B-A3B-Uncensored-GGUF:Q4_K_M \
  --jinja --reasoning-format deepseek --port 8000

For image input, also load mmproj-Ornith-1.5-35B-Uncensored-BF16.gguf.

Recommended upstream sampling: temperature=0.6, top_p=0.95, top_k=20. Ornith is a reasoning model and emits <think>...</think> before the answer.

Notes

"Uncensored" means the refusal behavior was deliberately reduced. It does not mean every request will be answered, nor that upstream benchmark scores are guaranteed unchanged. Ornith's published benchmark table has not been claimed as a benchmark of this derivative; use the included build and evaluation reports for claims specific to this release.

Credits and licenses

Ornith Team: Ornith-1.5 (model card declares MIT)
Qwen Team: Qwen3.6-35B-A3B (Apache-2.0)
wangzhang / Abliterix: the abliterated Qwen donor and documented method
ggml-org: llama.cpp conversion and quantization tooling

The Qwen Apache-2.0 license and upstream notices are included with the release.

Downloads last month: 18,609

GGUF

Model size

36B params

Architecture

qwen35moe

Hardware compatibility

4-bit

5-bit

16-bit

Model tree for 0xKitkat/Ornith-1.5-35B-A3B-Uncensored-GGUF

Base model

Qwen/Qwen3.6-35B-A3B

Quantized

(776)

this model