Text Generation
Transformers
Safetensors
qwen3_5_moe
image-text-to-text
abliterated
uncensored
refusal-removed
abliterix
aeon
aeon-7
gated-deltanet
hybrid
Mixture of Experts
mixture-of-experts
reasoning
thinking
coding
agentic
swe-bench
terminal-bench
tool-calling
vision
multimodal
norm-preserving-biprojection
expert-granular-abliteration
vllm
dgx-spark
gb10
bfloat16
conversational
35b
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
- Transformers
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") 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

- Xet hash:
- 3d6797eaa4e1e96806c5f575f9ce941a0e4448ee5f2e0e29598d91fa63f9b758
- Size of remote file:
- 507 kB
- SHA256:
- 277bacf4dd0c9cb7e6e5d692c01c0751e05170be2d9d0e4e9702dff01e7f2c48
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