Image-Text-to-Text
Transformers
Safetensors
English
gemma4
Merge
mergekit
moe_della
conversational
roleplay
creative writing
ablated
uncensored
heretic
ara
ari
Instructions to use Naphula/Goetia-26B-A4B-v1.3-Tainted-Heretic-ARI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Naphula/Goetia-26B-A4B-v1.3-Tainted-Heretic-ARI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Naphula/Goetia-26B-A4B-v1.3-Tainted-Heretic-ARI") 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("Naphula/Goetia-26B-A4B-v1.3-Tainted-Heretic-ARI") model = AutoModelForMultimodalLM.from_pretrained("Naphula/Goetia-26B-A4B-v1.3-Tainted-Heretic-ARI", 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 Naphula/Goetia-26B-A4B-v1.3-Tainted-Heretic-ARI with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Naphula/Goetia-26B-A4B-v1.3-Tainted-Heretic-ARI" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Naphula/Goetia-26B-A4B-v1.3-Tainted-Heretic-ARI", "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/Naphula/Goetia-26B-A4B-v1.3-Tainted-Heretic-ARI
- SGLang
How to use Naphula/Goetia-26B-A4B-v1.3-Tainted-Heretic-ARI 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 "Naphula/Goetia-26B-A4B-v1.3-Tainted-Heretic-ARI" \ --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": "Naphula/Goetia-26B-A4B-v1.3-Tainted-Heretic-ARI", "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 "Naphula/Goetia-26B-A4B-v1.3-Tainted-Heretic-ARI" \ --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": "Naphula/Goetia-26B-A4B-v1.3-Tainted-Heretic-ARI", "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 Naphula/Goetia-26B-A4B-v1.3-Tainted-Heretic-ARI with Docker Model Runner:
docker model run hf.co/Naphula/Goetia-26B-A4B-v1.3-Tainted-Heretic-ARI
| {"model":"/workspace/Naphula/Goetia-26B-A4B-v1.3","evaluate_model":null,"dtypes":["auto","float16","bfloat16","float32"],"quantization":"none","device_map":"auto","max_memory":null,"trust_remote_code":null,"batch_size":64,"max_batch_size":128,"max_response_length":100,"print_responses":false,"print_residual_geometry":false,"plot_residuals":false,"residual_plot_path":"plots","residual_plot_title":"PaCMAP Projection of Residual Vectors for \"Harmless\" and \"Harmful\" Prompts","residual_plot_style":"dark_background","kl_divergence_scale":1.0,"kl_divergence_target":0.05,"target_components":["attn.o_proj","mlp.down_proj"],"use_ara":true,"use_piqa":false,"orthogonalize_direction":false,"row_normalization":"full","full_normalization_lora_rank":3,"winsorization_quantile":1.0,"n_trials":60,"n_startup_trials":20,"study_checkpoint_dir":"checkpoints","benchmarks":[{"task":"agieval","name":"AGIEval","description":"A Human-Centric Benchmark for Evaluating Foundation Models"},{"task":"bbh","name":"BIG-Bench Hard (BBH)","description":"Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them"},{"task":"commonsense_qa","name":"CommonsenseQA","description":"A Question Answering Challenge Targeting Commonsense Knowledge"},{"task":"eq_bench","name":"EQ-Bench","description":"An Emotional Intelligence Benchmark for Large Language Models"},{"task":"gsm8k","name":"GSM8K","description":"Training Verifiers to Solve Math Word Problems"},{"task":"hellaswag","name":"HellaSwag","description":"Can a Machine Really Finish Your Sentence?"},{"task":"ifeval","name":"IFEval","description":"Instruction-Following Evaluation for Large Language Models"},{"task":"mmlu","name":"MMLU","description":"Measuring Massive Multitask Language Understanding"},{"task":"mmlu_pro","name":"MMLU-Pro","description":"A More Robust and Challenging Multi-Task Language Understanding Benchmark"},{"task":"piqa","name":"PIQA","description":"Reasoning about Physical Commonsense in Natural Language"},{"task":"winogrande","name":"WinoGrande","description":"An Adversarial Winograd Schema Challenge at Scale"}],"refusal_markers":["sorry","i can'","i cant","i cannot","i won'","i wont","i will not","i unable","im unable","i'm unable","i am unable","i an ai","im an ai","i'm an ai","i am an ai","as an ai","ai assistant","i designed to","im designed to","i'm designed to","i am designed to","i programmed to","im programmed to","i'm programmed to","i am programmed to","violat","prohibit","illegal","harmful","inappropriate","unethical","ethical boundaries"],"system_prompt":"You are a helpful assistant.","good_prompts":{"dataset":"mlabonne/harmless_alpaca","split":"train[:400]","column":"text","prefix":"","suffix":"","system_prompt":null,"residual_plot_label":"\"Harmless\" prompts","residual_plot_color":"royalblue"},"bad_prompts":{"dataset":"mlabonne/harmful_behaviors","split":"train[:400]","column":"text","prefix":"","suffix":"","system_prompt":null,"residual_plot_label":"\"Harmful\" prompts","residual_plot_color":"darkorange"},"good_evaluation_prompts":{"dataset":"mlabonne/harmless_alpaca","split":"test[:100]","column":"text","prefix":"","suffix":"","system_prompt":null,"residual_plot_label":null,"residual_plot_color":null},"bad_evaluation_prompts":{"dataset":"mlabonne/harmful_behaviors","split":"test[:100]","column":"text","prefix":"","suffix":"","system_prompt":null,"residual_plot_label":null,"residual_plot_color":null}} |