Instructions to use PocketAiHub/Ornith-1.5-9B-Abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PocketAiHub/Ornith-1.5-9B-Abliterated with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="PocketAiHub/Ornith-1.5-9B-Abliterated")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("PocketAiHub/Ornith-1.5-9B-Abliterated")
model = AutoModelForMultimodalLM.from_pretrained("PocketAiHub/Ornith-1.5-9B-Abliterated", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use PocketAiHub/Ornith-1.5-9B-Abliterated with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PocketAiHub/Ornith-1.5-9B-Abliterated"
# 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-1.5-9B-Abliterated",
		"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-1.5-9B-Abliterated

SGLang

How to use PocketAiHub/Ornith-1.5-9B-Abliterated with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "PocketAiHub/Ornith-1.5-9B-Abliterated" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PocketAiHub/Ornith-1.5-9B-Abliterated",
		"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 images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "PocketAiHub/Ornith-1.5-9B-Abliterated" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PocketAiHub/Ornith-1.5-9B-Abliterated",
		"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"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use PocketAiHub/Ornith-1.5-9B-Abliterated with Docker Model Runner:
```
docker model run hf.co/PocketAiHub/Ornith-1.5-9B-Abliterated
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Ornith 1.5 9B Abliterated — Transformers safetensors BF16

An unofficial experimental derivative of ornith-ai/Ornith-1.5-9B, pinned to revision c927ad73b7eb20f00aafcaa0a11a9d58ed5487bc. The original model is by the Ornith team. The conversion, refusal-direction experiment, and validation were performed by PocketAI Model Lab; PocketAiHub identifies the publisher of this derivative.

Purpose and responsible use

This experimental derivative studies whether learned refusal behavior can be reduced while retaining general capability. It is published for research and legitimate local use, not to endorse or facilitate illegal, abusive, or dangerous applications.

The edit reduces refusal behavior broadly rather than determining whether a request is legitimate. Deployers should evaluate the model in their own context and apply appropriate safeguards. Abliteration is not truthfulness training, a capability improvement, or a guarantee of universal compliance.

Release family

Format and recipe

Format: Transformers safetensors
Precision: BF16
Abliteration scale: 1.0
Direction source layer: 23
Destination layers: 12–31
Modified residual-output tensors: 40
Native MTP is not included
Native Transformers text loading passed; the matching MLX and GGUF releases received image-input smoke tests.
Peak runtime memory in the smoke test: 19.05 GB

Validation

Gate	Result
Refusal-targeted explicit-refusal phrase flags	0/100
Benign-control explicit-refusal phrase flags	0/100
Medium capability suite	71/80
Runtime smoke	passed

The medium suite covers math/reasoning, false-premise handling, instruction following, coding, structured output, multilingual output, context comprehension, and general coherence.

The refusal scorer is phrase based and can miss redirects and other non-literal forms of non-compliance. Therefore 0/100 phrase flags measures explicit refusal wording, not universal compliance or response quality. The 256-token runs are early-response screens rather than complete long-answer evaluations.

See abliteration-manifest.json and validation-summary.json for machine-readable provenance and category-level results.

Load with Transformers

from transformers import AutoProcessor, Qwen3_5ForConditionalGeneration

repo = "PocketAiHub/Ornith-1.5-9B-Abliterated"
processor = AutoProcessor.from_pretrained(repo)
model = Qwen3_5ForConditionalGeneration.from_pretrained(repo, device_map="auto")

License

The upstream model card declares MIT. This repository includes the MIT license and preserves attribution to the pinned source above.

Downloads last month: 18

Safetensors

Model size

9B params

Tensor type

BF16

Model tree for PocketAiHub/Ornith-1.5-9B-Abliterated

Base model

ornith-ai/Ornith-1.5-9B

Finetuned

(7)

this model

Quantizations

2 models