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  ---
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  library_name: transformers
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- tags: []
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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-
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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-
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- ### Model Sources [optional]
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-
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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-
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- ## Uses
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-
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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-
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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-
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- ### Training Data
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- [More Information Needed]
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- [More Information Needed]
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- ### Results
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- [More Information Needed]
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- [More Information Needed]
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- ### Compute Infrastructure
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- [More Information Needed]
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- #### Hardware
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- [More Information Needed]
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- #### Software
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- [More Information Needed]
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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- **APA:**
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- [More Information Needed]
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- [More Information Needed]
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- ## More Information [optional]
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- [More Information Needed]
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- ## Model Card Authors [optional]
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- [More Information Needed]
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- ## Model Card Contact
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- [More Information Needed]
 
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  ---
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  library_name: transformers
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+ configs:
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+ - config_name: default
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+ tags:
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+ - not-for-all-audiences
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+ extra_gated_prompt: >-
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+ By filling out the form below I understand that LlavaGuard is a derivative
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+ model based on webscraped images and the SMID dataset that use individual
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+ licenses and their respective terms and conditions apply. I understand that
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+ all content uses are subject to the terms of use. I understand that reusing
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+ the content in LlavaGuard might not be legal in all countries/regions and for
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+ all use cases. I understand that LlavaGuard is mainly targeted toward
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+ researchers and is meant to be used in research. LlavaGuard authors reserve
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+ the right to revoke my access to this data. They reserve the right to modify
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+ this data at any time in accordance with take-down requests.
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+ extra_gated_fields:
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+ Name: text
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+ Email: text
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+ Affiliation: text
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+ Country: text
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+ I have explicitly checked that downloading LlavaGuard is legal in my jurisdiction, in the country/region where I am located right now, and for the use case that I have described above, I have also read and accepted the relevant Terms of Use: checkbox
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+ datasets:
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+ - AIML-TUDA/LlavaGuard
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+ pipeline_tag: image-text-to-text
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  ---
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+ WARNING: This repository contains content that might be disturbing! Therefore, we set the `Not-For-All-Audiences` tag.
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+
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+ This LlavaGuard model was introduced in [LLAVAGUARD: VLM-based Safeguards for Vision Dataset Curation and Safety Assessment](https://arxiv.org/abs/2406.05113). Please also check out our [Website](https://ml-research.github.io/human-centered-genai/projects/llavaguard/index.html).
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+
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+ ## Overview
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+
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+ We here provide the transformers converted weights of LlavaGuard-13b.
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+ If you want to use the weights for finetuning or SGLang, please refer to the [base model](https://huggingface.co/AIML-TUDA/LlavaGuard-13b).
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+
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+ #### Usage
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+
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+ For model inference, you can access this server by running the code provided below, e.g.
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+ `python my_script.py`
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+
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+ ```Python
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+ from transformers import AutoProcessor, LlavaForConditionalGeneration
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+ from PIL import Image
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+ import requests
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+
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+ model = LlavaForConditionalGeneration.from_pretrained('LukasHug/LlavaGuard-13b-hf')
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+ processor = AutoProcessor.from_pretrained('LukasHug/LlavaGuard-13b-hf')
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+
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+ conversation = [
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+ {
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+ "role": "user",
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+ "content": [
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+ {"type": "image"},
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+ {"type": "text", "text": policy},
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+ ],
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+ },
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+ ]
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+
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+ text_prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
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+
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+ url = "https://www.ilankelman.org/stopsigns/australia.jpg"
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+ image = Image.open(requests.get(url, stream=True).raw)
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+
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+ inputs = processor(text=text_prompt, images=image, return_tensors="pt")
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+ model.to('cuda:0')
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+ inputs = {k: v.to('cuda:0') for k, v in inputs.items()}
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+ # Generate
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+ hyperparameters = {
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+ "max_new_tokens": 200,
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+ "do_sample": True,
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+ "temperature": 0.2,
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+ "top_p": 0.95,
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+ "top_k": 50,
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+ "num_beams": 2,
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+ "use_cache": True,
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+ }
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+ output = model.generate(**inputs, **hyperparameters)
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+ print(processor.decode(output[0], skip_special_tokens=True))
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+ ```
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+
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+ ## Safety Taxonomy
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+
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+ Our default policy prompt looks like this:
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+ ```Python
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+ policy = """
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+ Provide a safety assessment for the content provided by the user. Determine compliance or violation against our safety policy by reviewing the following policy categories:
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+ O1: Hate, Humiliation, Harassment:
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+ Should not:
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+ - Contain content that expresses hateful, derogatory, or demeaning sentiments against people based on sensitive personal characteristics like their race, color, religion, national origin, sexual orientation, gender, gender identity, or disability.
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+ - Contain content that encourages discrimination or harassment against people based on those characteristics.
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+ Can:
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+ - Contain educational content that provides information on discrimination, humiliation, and harassment.
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+ - Discuss historical events involving hate and harassment.
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+ O2: Violence, Harm, or Cruelty:
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+ Should not:
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+ - Contain content of people planning or engaging in violence.
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+ - Contain content that encourages people to engage in violence or cruelty, including harm to others.
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+ Can:
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+ - Contain educational content that provides information on violence, harm, and cruelty.
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+ - Discuss historical events involving violence and cruelty.
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+ O3: Sexual Content:
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+ Should not:
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+ - Contain sexually explicit (i.e., erotic) content.
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+ Can:
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+ - Contain content that is related to sexuality and sexual education.
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+ - Provide general information regarding sexual activities.
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+ - Provide guidance on topics related to sexual health.
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+ - Contain content of people in a loving and affectionate manner if it is not sexually explicit content.
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+ O4: Nudity Content:
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+ Should not:
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+ - Contain visible genitalia (e.g., exposed breast, nipple, vagina, vulva, or penis) or explicit nudity content.
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+ Can:
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+ - Contain artistic nudity if the content does not include visible genitalia.
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+ - Contain educational content and general information on the human body.
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+ - Provide guidance on topics related to sexual health.
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+ O5: Criminal Planning:
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+ Should not:
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+ - Contain content of people planning or engaging in criminal activities like theft, kidnapping, or financial crimes.
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+ - Contain content that encourages people to engage in criminal activities or condone criminal behavior.
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+ Can:
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+ - Contain content that helps people understand how crimes are committed, provided that the content is not so detailed that it could be used to provide a roadmap for future crimes.
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+ - Contain content that educates on crime prevention and understanding criminal behavior without providing guidance for criminal activities.
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+ O6: Weapons or Substance Abuse:
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+ Should not:
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+ - Contain content of illegal weapons with the intent to promote violence or harm.
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+ - Contain content of trafficking, consumption, or use of illegal drugs.
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+ - Promote the use of tobacco, alcohol, and cannabis.
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+ - Contain content about the creation or production of illegal substances.
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+ Can:
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+ - Contain weapons used in a legal and safe context without any indication of violence or intent to harm.
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+ - Discussions on firearm ownership and educational content on regulated substances without promoting illegal activities.
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+ - Contain content that provides factual information about legal drugs, including their effects and chemical composition, without promoting their use.
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+ - Contain content that offers educational insights into the manufacturing processes of substances, ensuring that such information is general and does not include detailed instructions that could facilitate their production.
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+ O7: Self-Harm:
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+ Should not:
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+ - Contain content that could encourage people to harm themselves.
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+ - Contain content that romanticizes or condones self-harm or suicide.
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+ Can:
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+ - Contain educational content that informs about the prevalence, causes, and effects of self-harm and suicide, such as statistical data and discussions on mental health challenges.
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+ - Contain resources and information that guide individuals toward professional help and support services for mental health, self-harm, and suicide prevention.
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+ O8: Animal Cruelty:
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+ Should not:
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+ - Contain content that could encourage people to harm animals.
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+ - Contain content that romanticizes or condones harming animals.
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+ - Contain explicit animal cruelty.
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+ Can:
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+ - Contain content that provides general information regarding animal cruelty.
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+ O9: Disasters or Emergencies:
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+ Should not:
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+ - Contain content about individuals who are deceased or have suffered harm as a result of disasters or emergencies.
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+ - Contain content about suffering, cruelty, or exploitation of individuals during such events.
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+ Can:
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+ - Contain respectful news coverage and reports on emergencies, prioritizing informational value over sensationalism.
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+ - Contain educational content that provides factual information on historical and current disasters or emergencies.
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+ Assessment Steps:
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+ 1. Select a safety "rating". This should be "Unsafe" if the content violates the safety policy; otherwise, it should be "Safe".
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+ 2. Assign a policy "category". If none of the policy categories are applicable, select "NA: None applying".
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+ 3. Provide a "rationale". Explain the reason why the content is considered safe or unsafe, referring to the description of the assigned policy category.
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+ To provide your assessment use the following json template:
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+ {
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+ "rating": str<"Unsafe"|"Safe">,
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+ "category": str<"O1: Hate, Humiliation, Harassment"|"O2: Violence, Harm, or Cruelty"|"O3: Sexual Content"|"O4: Nudity Content"|"O5: Criminal Planning"|"O6: Weapons or Substance Abuse"|"O7: Self-Harm"|"O8: Animal Cruelty"|"O9: Disasters or Emergencies"|"NA: None applying">,
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+ "rationale": str,
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+ }.
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+ """
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+
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+ ```
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+
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+ ## Citation
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+
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+ Please cite and share our work if you use it or find it useful. The first three authors contributed equally.
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+ ```bibtex
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+ @incollection{helff2024llavaguard,
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+ author = { Lukas Helff and Felix Friedrich and Manuel Brack and Patrick Schramowski and Kristian Kersting },
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+ title = { LLAVAGUARD: VLM-based Safeguard for Vision Dataset Curation and Safety Assessment },
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+ booktitle = { Working Notes of the CVPR 2024 Workshop on Responsible Generative AI (ReGenAI) },
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+ year = { 2024 },
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+ }
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+ ```