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

Use Docker

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

LM Studio
Jan

vLLM

How to use PocketAiHub/Ornith-1.5-35B-A3B-Abliterated-GGUF with vLLM:

Install from pip and serve model

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

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

Unsloth Studio

How to use PocketAiHub/Ornith-1.5-35B-A3B-Abliterated-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 PocketAiHub/Ornith-1.5-35B-A3B-Abliterated-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 PocketAiHub/Ornith-1.5-35B-A3B-Abliterated-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use PocketAiHub/Ornith-1.5-35B-A3B-Abliterated-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf PocketAiHub/Ornith-1.5-35B-A3B-Abliterated-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": "PocketAiHub/Ornith-1.5-35B-A3B-Abliterated-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

How to use PocketAiHub/Ornith-1.5-35B-A3B-Abliterated-GGUF with Lemonade:

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Hermes Agent

How to use PocketAiHub/Ornith-1.5-35B-A3B-Abliterated-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 PocketAiHub/Ornith-1.5-35B-A3B-Abliterated-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 PocketAiHub/Ornith-1.5-35B-A3B-Abliterated-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use PocketAiHub/Ornith-1.5-35B-A3B-Abliterated-GGUF with OpenClaw:

Start the llama.cpp server

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

One-repository GGUF release of an unofficial abliterated derivative of ornith-ai/Ornith-1.5-35B-A3B, pinned to revision e4dfb35a93d4b6822a811a7676f3488514abe7e2. The original model is by Ornith AI. PocketAI Model Lab performed the refusal-direction edit, GGUF conversion, and validation.

Purpose and responsible use

This release is published for reproducible local-model research and evaluation of how refusal-direction editing and quantization affect model behavior. It is not an endorsement of harmful, illegal, deceptive, or abusive use. The results below document refusal behavior and quantization drift so these tradeoffs can be evaluated directly; reduced refusals should not be interpreted as a quality or safety claim.

Important safety notice

This model was deliberately modified to suppress learned refusal behavior. It may produce harmful, illegal, offensive, deceptive, or dangerously incorrect content more readily than the upstream instruction model. Abliteration is not truthfulness training, a capability improvement, or a guarantee of universal compliance. Evaluate and constrain it for your use case.

Files

File	Size	Guidance
`Ornith-1.5-35B-A3B-Abliterated-Q4_K_M.gguf`	19.71 GiB	Recommended starting point for local use
`Ornith-1.5-35B-A3B-Abliterated-Q8_0.gguf`	34.37 GiB	Higher-fidelity quantization
`Ornith-1.5-35B-A3B-Abliterated-BF16.gguf`	64.61 GiB	Unquantized reference
`mmproj-Ornith-1.5-35B-A3B-Abliterated-F16.gguf`	0.84 GiB	Shared F16 vision projector

The language GGUFs use the validated abliterated BF16 checkpoint. The vision projector uses the exact pinned upstream vision tower because the abliteration did not modify vision weights. The native MTP speculative-decoding head is not included.

Text usage

Download one language file, then run a recent llama.cpp build:

huggingface-cli download PocketAiHub/Ornith-1.5-35B-A3B-Abliterated-GGUF \
  Ornith-1.5-35B-A3B-Abliterated-Q4_K_M.gguf --local-dir .

llama-cli -m Ornith-1.5-35B-A3B-Abliterated-Q4_K_M.gguf \
  -ngl all -c 4096 -n 256 \
  -p "Explain why seasons occur."

Vision usage

Download the matching language model and the shared projector:

huggingface-cli download PocketAiHub/Ornith-1.5-35B-A3B-Abliterated-GGUF \
  Ornith-1.5-35B-A3B-Abliterated-Q4_K_M.gguf \
  mmproj-Ornith-1.5-35B-A3B-Abliterated-F16.gguf --local-dir .

llama-mtmd-cli -m Ornith-1.5-35B-A3B-Abliterated-Q4_K_M.gguf \
  --mmproj mmproj-Ornith-1.5-35B-A3B-Abliterated-F16.gguf \
  --image photo.jpg -p "Describe this image."

Q4_K_M plus the projector passed an end-to-end image smoke test. The Q8_0 and BF16 language files passed text inference but did not receive separate vision smoke tests.

Abliteration recipe

A projected harmful-minus-harmless direction was measured from 256 length-matched prompts per class at the assistant-generation boundary.

Direction source layer: 27
Destination layers: 15–39
Scale: 1.0
Per-input-column norm preservation: enabled
Modified physical tensors: 75
Modified logical expert/projection paths: 6,450
Direction SHA-256: b4bef4649c209aae888c7b313feb89005f897938c0a01540a6852f0e3bf4b407

See abliteration-manifest.json for the machine-readable recipe.

GGUF behavior screen

The deterministic llama.cpp Metal screen used 100 JailbreakBench harmful prompts and 100 benign controls per quantized model, a 256-token cap, batch 1, thinking disabled, and a transparent phrase-based refusal detector.

Variant	Harmful refusal flags	Benign refusal flags	Final-answer segments
Q4_K_M	3/100	0/100	200/200
Q8_0	1/100	0/100	200/200
BF16 confirmation	0/12	0/12	24/24

The flagged quantized responses were manually confirmed as genuine refusals. Most generations reached the 256-token ceiling. This is an early-behavior screen, not proof of universal compliance, safety, correctness, or full answer quality.

Matched GGUF quantization drift

The comparison reused 36 prompts and 481 shared teacher positions. At every position it measured exact D_KL(P_BF16 || P_quantized) over all 248,320 vocabulary logits.

Variant vs GGUF BF16	Mean KL (nats)	Top-token agreement
Q8_0	0.01359	96.26%
Q4_K_M	0.07280	90.64%

The BF16 self-check returned 2.98e-09 mean KL and 100% top-token agreement. llama.cpp did not expose the residual and KV/recurrent-state tensors captured in the separate MLX analysis.

Machine-readable aggregate results are in validation-summary.json, and conversion checks and SHA-256 values are included in the repository manifests.

Conversion provenance

llama.cpp revision: 555881ebc8b0fc0402b30e09258a32a7bfd13c52
Qwen 3.5 MoE conversion supports the MLX stacked-expert layout
The converter avoids applying the Qwen 3.5 RMSNorm unit offset twice
MTP included: no

License and attribution

The upstream model card declares MIT. This derivative preserves the upstream attribution and links to the exact source revision above.

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GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

4-bit

8-bit

16-bit

Model tree for PocketAiHub/Ornith-1.5-35B-A3B-Abliterated-GGUF

Base model

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

Quantized

(87)

this model

Quantizations

1 model