Instructions to use AEON-7/Ornith-1.0-35B-AEON-Ultimate-Uncensored-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AEON-7/Ornith-1.0-35B-AEON-Ultimate-Uncensored-BF16 with Transformers:

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

pipe = pipeline("text-generation", model="AEON-7/Ornith-1.0-35B-AEON-Ultimate-Uncensored-BF16")
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("AEON-7/Ornith-1.0-35B-AEON-Ultimate-Uncensored-BF16")
model = AutoModelForMultimodalLM.from_pretrained("AEON-7/Ornith-1.0-35B-AEON-Ultimate-Uncensored-BF16", 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 AEON-7/Ornith-1.0-35B-AEON-Ultimate-Uncensored-BF16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AEON-7/Ornith-1.0-35B-AEON-Ultimate-Uncensored-BF16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AEON-7/Ornith-1.0-35B-AEON-Ultimate-Uncensored-BF16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AEON-7/Ornith-1.0-35B-AEON-Ultimate-Uncensored-BF16

SGLang

How to use AEON-7/Ornith-1.0-35B-AEON-Ultimate-Uncensored-BF16 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 "AEON-7/Ornith-1.0-35B-AEON-Ultimate-Uncensored-BF16" \
    --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": "AEON-7/Ornith-1.0-35B-AEON-Ultimate-Uncensored-BF16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "AEON-7/Ornith-1.0-35B-AEON-Ultimate-Uncensored-BF16" \
        --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": "AEON-7/Ornith-1.0-35B-AEON-Ultimate-Uncensored-BF16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AEON-7/Ornith-1.0-35B-AEON-Ultimate-Uncensored-BF16 with Docker Model Runner:
```
docker model run hf.co/AEON-7/Ornith-1.0-35B-AEON-Ultimate-Uncensored-BF16
```

AEON-7 commited on Jun 27

Commit

b11282e

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1 Parent(s): fceb120

tipjar + NVFP4 variant note

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@@ -80,6 +80,16 @@ Reasoning model: every turn opens `<think>…</think>`. Recommended sampling: `t
 This model has had safety refusals removed and **will comply with harmful requests**. It is released for research into model alignment, red-teaming, and uncensored assistants. **You are solely responsible** for what you generate and how you use it; comply with all applicable laws. No warranty.
 ## Provenance & credits
 - Base: [deepreinforce-ai/Ornith-1.0-35B](https://huggingface.co/deepreinforce-ai/Ornith-1.0-35B) (MIT)
 - Driver: [abliterix](https://github.com/wuwangzhang1216/abliterix) (Wangzhang Wu) · upstream [heretic](https://github.com/p-e-w/heretic) (Philipp Emanuel Weidmann)

 This model has had safety refusals removed and **will comply with harmful requests**. It is released for research into model alignment, red-teaming, and uncensored assistants. **You are solely responsible** for what you generate and how you use it; comply with all applicable laws. No warranty.
+## ☕ Support the work
+[![Tips](https://img.shields.io/badge/%E2%98%95_Tips-Support_the_work-ff5e5b?style=flat)](https://github.com/AEON-7/AEON-7#-support-the-work)
+If this release is useful, tips fuel more compute and more open models — thank you. [QR codes on the profile »](https://github.com/AEON-7/AEON-7#-support-the-work)
+- **₿ BTC** — `bc1q09xmzn00q4z3c5raene0f3pzn9d9pvawfm0py4`
+- **Ξ ETH** — `0x1512667F6D61454ad531d2E45C0a5d1fd82D0500`
+- **◎ SOL** — `DgQsjHdAnT5PNLQTNpJdpLS3tYGpVcsHQCkpoiAKsw8t`
+- **ⓜ XMR** — `836XrSKw4R76vNi3QPJ5Fa9ugcyvE2cWmKSPv3AhpTNNKvqP8v5ba9JRL4Vh7UnFNjDz3E2GXZDVVenu3rkZaNdUFhjAvgd`
 ## Provenance & credits
 - Base: [deepreinforce-ai/Ornith-1.0-35B](https://huggingface.co/deepreinforce-ai/Ornith-1.0-35B) (MIT)
 - Driver: [abliterix](https://github.com/wuwangzhang1216/abliterix) (Wangzhang Wu) · upstream [heretic](https://github.com/p-e-w/heretic) (Philipp Emanuel Weidmann)