AIML-TUDA
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LlavaGuard-v1.2-0.5B-OV-hf

Image-Text-to-Text

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llava_onevision

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 ---
-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-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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-## Uses
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-## How to Get Started with the Model
-Use the code below to get started with the model.
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-## Training Details
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-## Environmental Impact
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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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 ---
+configs:
+- config_name: default
+extra_gated_prompt: >-
+  By filling out the form below I understand that LlavaGuard is a derivative
+  model based on webscraped images and the SMID dataset that use individual
+  licenses and their respective terms and conditions apply. I understand that
+  all content uses are subject to the terms of use. I understand that reusing
+  the content in LlavaGuard might not be legal in all countries/regions and for
+  all use cases. I understand that LlavaGuard is mainly targeted toward
+  researchers and is meant to be used in research. LlavaGuard authors reserve
+  the right to revoke my access to this data. They reserve the right to modify
+  this data at any time in accordance with take-down requests.
+extra_gated_fields:
+  Name: text
+  Email: text
+  Affiliation: text
+  Country: text
+  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
+datasets:
+- AIML-TUDA/LlavaGuard
+pipeline_tag: image-text-to-text
+base_model:
+- lmms-lab/llava-onevision-qwen2-0.5b-ov
 ---
+## Model Summary
+LlavaGuard-v1.2-0.5B-OV is trained on [LlavaGuard-DS](https://huggingface.co/datasets/AIML-TUDA/LlavaGuard) and based on llava-onevision-qwen2-0.5b-ov model with a context window of 32K tokens. Our smallest model allows for more efficient inference while maintaining a strong performance.
+- Links to Model Versions: [sglang](https://huggingface.co/datasets/AIML-TUDA/LlavaGuard-v1.2-0.5B-OV), [tranformers](https://huggingface.co/datasets/AIML-TUDA/LlavaGuard-v1.2-0.5B-OV-HF)
+- Repository: [ml-research/LlavaGuard](https://github.com/ml-research/LlavaGuard)
+- Project Website: [LlavaGuard](https://ml-research.github.io/human-centered-genai/projects/llavaguard/index.html)
+- Paper: [LlavaGuard-Arxiv](https://arxiv.org/abs/2406.05113)
+## Model Compatability
+- Inference: HF Tranformers✅, SGLang❌, LLaVA [repo](https://github.com/LLaVA-VL/LLaVA-NeXT)❌
+- Model Tuning:❌
+## Overview
+We here provide the transformers converted weights for LlavaGuard v1.2 0.5B.
+It builds upon LLaVA-OneVision 0.5B and has achieved the best overall performance so far with improved reasoning capabilities within the rationales.
+#### Usage
+For model inference, you can access this server by running the code provided below, e.g.
+`python my_script.py`
+```Python
+from transformers import AutoProcessor, LlavaOnevisionForConditionalGeneration
+from PIL import Image
+import requests
+model = LlavaOnevisionForConditionalGeneration.from_pretrained('AIML-TUDA/LlavaGuard-v1.2-0.5B-OV-hf')
+processor = AutoProcessor.from_pretrained('AIML-TUDA/LlavaGuard-v1.2-0.5B-OV-hf')
+conversation = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image"},
+            {"type": "text", "text": policy},
+            ],
+    },
+]
+text_prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
+url = "https://www.ilankelman.org/stopsigns/australia.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+inputs = processor(text=text_prompt, images=image, return_tensors="pt")
+model.to('cuda:0')
+inputs = {k: v.to('cuda:0') for k, v in inputs.items()}
+# Generate
+hyperparameters = {
+    "max_new_tokens": 200,
+    "do_sample": True,
+    "temperature": 0.2,
+    "top_p": 0.95,
+    "top_k": 50,
+    "num_beams": 2,
+    "use_cache": True,
+}
+output = model.generate(**inputs, **hyperparameters)
+print(processor.decode(output[0], skip_special_tokens=True))
+```
+## Safety Taxonomy
+Our default policy prompt looks like this:
+```Python
+prompt = """
+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:
+O1: Hate, Humiliation, Harassment:
+Should not:
+- 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.
+- Contain content that encourages discrimination or harassment against people based on those characteristics.
+Can:
+- Contain educational content that provides information on discrimination, humiliation, and harassment.
+- Discuss historical events involving hate and harassment.
+O2: Violence, Harm, or Cruelty:
+Should not:
+- Contain content of people planning or engaging in violence.
+- Contain content that encourages people to engage in violence or cruelty, including harm to others.
+Can:
+- Contain educational content that provides information on violence, harm, and cruelty.
+- Discuss historical events involving violence and cruelty.
+O3: Sexual Content:
+Should not:
+- Contain sexually explicit (i.e., erotic) content.
+Can:
+- Contain content that is related to sexuality and sexual education.
+- Provide general information regarding sexual activities.
+- Provide guidance on topics related to sexual health.
+- Contain content of people in a loving and affectionate manner if it is not sexually explicit content.
+O4: Nudity Content:
+Should not:
+- Contain visible genitalia (e.g., exposed breast, nipple, vagina, vulva, or penis) or explicit nudity content.
+Can:
+- Contain artistic nudity if the content does not include visible genitalia.
+- Contain educational content and general information on the human body.
+- Provide guidance on topics related to sexual health.
+O5: Criminal Planning:
+Should not:
+- Contain content of people planning or engaging in criminal activities like theft, kidnapping, or financial crimes.
+- Contain content that encourages people to engage in criminal activities or condone criminal behavior.
+Can:
+- 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.
+- Contain content that educates on crime prevention and understanding criminal behavior without providing guidance for criminal activities.
+O6: Weapons or Substance Abuse:
+Should not:
+- Contain content of illegal weapons with the intent to promote violence or harm.
+- Contain content of trafficking, consumption, or use of illegal drugs.
+- Promote the use of tobacco, alcohol, and cannabis.
+- Contain content about the creation or production of illegal substances.
+Can:
+- Contain weapons used in a legal and safe context without any indication of violence or intent to harm.
+- Discussions on firearm ownership and educational content on regulated substances without promoting illegal activities.
+- Contain content that provides factual information about legal drugs, including their effects and chemical composition, without promoting their use.
+- 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.
+O7: Self-Harm:
+Should not:
+- Contain content that could encourage people to harm themselves.
+- Contain content that romanticizes or condones self-harm or suicide.
+Can:
+- 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.
+- Contain resources and information that guide individuals toward professional help and support services for mental health, self-harm, and suicide prevention.
+O8: Animal Cruelty:
+Should not:
+- Contain content that could encourage people to harm animals.
+- Contain content that romanticizes or condones harming animals.
+- Contain explicit animal cruelty.
+Can:
+- Contain content that provides general information regarding animal cruelty.
+O9: Disasters or Emergencies:
+Should not:
+- Contain content about individuals who are deceased or have suffered harm as a result of disasters or emergencies.
+- Contain content about suffering, cruelty, or exploitation of individuals during such events.
+Can:
+- Contain respectful news coverage and reports on emergencies, prioritizing informational value over sensationalism.
+- Contain educational content that provides factual information on historical and current disasters or emergencies.
+Assessment Steps:
+1. Select a safety "rating". This should be "Unsafe" if the content violates the safety policy; otherwise, it should be "Safe".
+2. Assign a policy "category". If none of the policy categories are applicable, select "NA: None applying".
+3. Provide a "rationale". Describe the user content and justify why it is considered safe or unsafe, referring to the specific policy category and its associated guidelines to illustrate any violations or compliance.
+To provide your assessment use the following json template:
+{
+    "rating": str<"Unsafe"|"Safe">,
+    "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">,
+    "rationale": str,
+}
+"""
+```
+## Citation
+Please cite and share our work if you use it or find it useful. The first three authors contributed equally.
+```bibtex
+@incollection{helff2024llavaguard,
+  author = { Lukas Helff and Felix Friedrich and Manuel Brack and Patrick Schramowski and Kristian Kersting },
+  title = { LLAVAGUARD: VLM-based Safeguard for Vision Dataset Curation and Safety Assessment },
+  booktitle = { Working Notes of the CVPR 2024 Workshop on Responsible Generative AI (ReGenAI) },
+  year = { 2024 },
+}
+```