| --- |
| license: apache-2.0 |
| base_model: |
| - akpsahan/Blabela-1.5V |
| pipeline_tag: image-text-to-text |
| tags: |
| - uncensored |
| - multimodal |
| - vision |
| --- |
| |
| # Blabela-1.5V (Uncensored) |
|
|
| ## Model Description |
|
|
| This is an **uncensored** multimodal vision-language model based on [`akpsahan/Blabela-1.5V`](https://huggingface.co/akpsahan/Blabela-1.5V). The standard safety guardrails and alignment filters have been removed or modified to allow for unrestricted outputs. |
|
|
| This model is designed for open research, creative writing, and advanced multimodal applications where unfiltered responses are required. It uses the `image-text-to-text` pipeline, allowing it to process both images and text inputs simultaneously to generate text-based answers, image captions, and visual analysis. |
|
|
| ## Model Details |
|
|
| * **Base Model:** [-] |
| * **License:** Apache 2.0 |
| * **Pipeline Tag:** image-text-to-text |
| * **Model Type:** Multimodal Large Language Model (MLLM) |
|
|
| ## ⚠️ Limitations & Disclaimer |
|
|
| * **Uncensored Nature:** Because this model is uncensored, it does not possess built-in safety filters. It may generate content that is considered toxic, offensive, biased, or inappropriate depending on the prompt provided. |
| * **User Responsibility:** The creators of this repository are not responsible for the outputs generated by this model. Users are strictly advised to use this model responsibly and ethically. |
| * **Deployment Warning:** Do NOT deploy this model in public-facing applications without implementing your own downstream safety filters and moderation guardrails. |
|
|
| ## How to Use |
|
|
| You can load and use this model via the standard Hugging Face `transformers` library. *(Note: Adjust the processor and model classes if Blabela-1.5V uses a specific architecture like LLaVA or Qwen-VL).* |
|
|
| ```python |
| from transformers import AutoProcessor, AutoModelForCausalLM |
| from PIL import Image |
| import requests |
| |
| # Replace with your actual Hugging Face model repository name |
| model_id = "your-username/your-model-name" |
| |
| processor = AutoProcessor.from_pretrained(model_id) |
| model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto") |
| |
| # Load an image |
| image_url = "[https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true)" |
| image = Image.open(requests.get(image_url, stream=True).raw) |
| |
| # Define your prompt |
| prompt = "Describe what is happening in this image." |
| |
| # Prepare inputs |
| inputs = processor(text=prompt, images=image, return_tensors="pt").to(model.device) |
| |
| # Generate response |
| outputs = model.generate(**inputs, max_new_tokens=150) |
| generated_text = processor.decode(outputs[0], skip_special_tokens=True) |
| |
| print(generated_text) |
