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

Libraries

How to use junafinity/Ornith-1.5-9B-uncensored with Transformers:

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

pipe = pipeline("image-text-to-text", model="junafinity/Ornith-1.5-9B-uncensored")
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("junafinity/Ornith-1.5-9B-uncensored")
model = AutoModelForMultimodalLM.from_pretrained("junafinity/Ornith-1.5-9B-uncensored", 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 junafinity/Ornith-1.5-9B-uncensored with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "junafinity/Ornith-1.5-9B-uncensored"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "junafinity/Ornith-1.5-9B-uncensored",
		"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/junafinity/Ornith-1.5-9B-uncensored

SGLang

How to use junafinity/Ornith-1.5-9B-uncensored 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 "junafinity/Ornith-1.5-9B-uncensored" \
    --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": "junafinity/Ornith-1.5-9B-uncensored",
		"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 "junafinity/Ornith-1.5-9B-uncensored" \
        --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": "junafinity/Ornith-1.5-9B-uncensored",
		"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 junafinity/Ornith-1.5-9B-uncensored with Docker Model Runner:
```
docker model run hf.co/junafinity/Ornith-1.5-9B-uncensored
```

Ornith-1.5-9B-uncensored / README.md

junafinity

Ornith 1.5 9B uncensored — full-precision abliterated weights (vision preserved)

55e0a71 verified about 10 hours ago

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	---
	license: apache-2.0
	base_model: ornith-ai/Ornith-1.5-9B
	library_name: transformers
	pipeline_tag: image-text-to-text
	tags:
	- ornith
	- qwen3_5
	- abliterated
	- uncensored
	- zerofuse
	- multimodal
	- vision
	---

	# Ornith-1.5-9B-uncensored

	An abliterated (refusal-direction-ablated) build of
	[`ornith-ai/Ornith-1.5-9B`](https://huggingface.co/ornith-ai/Ornith-1.5-9B), produced with
	[ZeroFuse](https://github.com/junainfinity/ZeroFuse) and published by
	[junafinity](https://huggingface.co/junafinity).

	Vision tower and MTP heads are preserved — see
	[Vision & MTP preservation](#vision--mtp-preservation) for the before/after audit.

	## Variants in this family

	\| Model \| Base \| Format \| Precision \| Notes \|
	\|---\|---\|---\|---\|---\|
	\| Ornith-1.5-9B-uncensored ← you are here \| Ornith-1.5-9B \| Safetensors (bf16) \| 16-bit \| Full-precision abliterated weights \|
	\| [Ornith-1.5-9B-uncensored-MLX-8bit](https://huggingface.co/junafinity/Ornith-1.5-9B-uncensored-MLX-8bit) \| Ornith-1.5-9B \| MLX \| 8-bit \| Apple-silicon MLX build \|
	\| [Ornith-1.5-9B-uncensored-GGUF-8bit](https://huggingface.co/junafinity/Ornith-1.5-9B-uncensored-GGUF-8bit) \| Ornith-1.5-9B \| GGUF \| Q8_0 \| llama.cpp build \|
	\| [Ornith-1.5-35B-A3B-uncensored-MLX-8bit](https://huggingface.co/junafinity/Ornith-1.5-35B-A3B-uncensored-MLX-8bit) \| Ornith-1.5-35B-A3B \| MLX \| 8-bit \| Apple-silicon MLX build \|
	\| [Ornith-1.5-35B-A3B-uncensored-GGUF-8bit](https://huggingface.co/junafinity/Ornith-1.5-35B-A3B-uncensored-GGUF-8bit) \| Ornith-1.5-35B-A3B \| GGUF \| Q8_0 \| llama.cpp build \|

	## Vision & MTP preservation

	Both the vision tower and any multi-token-prediction (MTP) block are preserved.
	Abliteration is applied only to the residual-writing projections inside the
	language-model decoder stack — `self_attn.o_proj`, `linear_attn.out_proj` and
	`mlp.down_proj` (including MoE experts). The vision tower and `mtp.*` tensors are
	never read and never written by the weight edit, so they carry through unchanged
	by construction.

	Audited at the start and end of the abliteration run:

	\| Component \| Before \| After \| Status \|
	\|---\|---\|---\|---\|
	\| Vision tower \| 333 tensors / 456,010,480 params \| 333 tensors / 456,010,480 params \| ✅ preserved — bit-identical \|
	\| MTP head \| not present in base \| not present \| ➖ none in this lineage \|

	Verification performed:

	- Tensor-name and parameter-count audit of the checkpoint before and after the run.
	- SHA-256 comparison of raw tensor bytes: sampled vision-tower weights are bit-identical pre/post, as are all non-target language-model weights; only the intended abliteration targets differ.
	- End-to-end multimodal generation on the abliterated weights (image in → description out), confirming the vision path is not merely present but functional.

	> On MTP, precisely: the base checkpoint's `config.json` declares `mtp_num_hidden_layers: 1`, but the published weights ship no `mtp.` tensors — there is no MTP block in this lineage to begin with. Nothing was removed and nothing was lost; the pipeline preserves `mtp.` tensors wherever a checkpoint actually provides them.

	## Abliteration result

	\| Metric \| Value \|
	\|---\|---\|
	\| Refusals on held-out harmful set \| 9 → 0 / 64 \|
	\| KL divergence from base \| 0.001668 \|
	\| Optuna trials \| 100 \|
	\| Pareto points \| 4 \|
	\| Selected trial \| #90 \|
	\| Ablation strength \| 1.343 \|
	\| Layers edited \| 15–20 of 32 \|
	\| Direction source layer \| 20 \|

	ZeroFuse co-minimizes two objectives — remaining refusals and KL divergence from the
	original model — with a multi-objective Optuna TPE search, then materializes the
	selected point on the Pareto front as a direct weight edit
	(`W' = W − strength · r(rᵀW)`). There is no runtime adapter and no inference-time
	overhead: the result is a standard checkpoint of identical shape and speed.

	The very low KL (0.001668) means the output distribution on harmless
	prompts is nearly unchanged from the base model, i.e. refusal behaviour was removed
	with minimal collateral effect on general capability.

	## Method

	1. Residual-stream activations captured on harmful vs. harmless prompt sets.
	2. Refusal direction estimated by difference-of-means, with projected refinement.
	3. Two-objective Optuna TPE search over source layer, layer span and strength.
	4. The selected configuration orthogonalized out of the residual-writing weights.

	## Usage

	```python
	from transformers import AutoModelForImageTextToText, AutoProcessor

	model = AutoModelForImageTextToText.from_pretrained(
	"junafinity/Ornith-1.5-9B-uncensored", dtype="auto", device_map="auto"
	)
	processor = AutoProcessor.from_pretrained("junafinity/Ornith-1.5-9B-uncensored")
	```

	> Requires `transformers >= 5.12` for the `qwen3_5` architecture.

	## Responsible use

	This model has had safety guardrails reduced or removed. You are responsible for
	compliance with the base model's license and acceptable-use policy, applicable law,
	and the terms of any platform you deploy on. Removing guardrails does not remove
	accountability.

	---

	*Abliterated with [ZeroFuse](https://github.com/junainfinity/ZeroFuse) · base model
	[ornith-ai/Ornith-1.5-9B](https://huggingface.co/ornith-ai/Ornith-1.5-9B)*