初始化项目，由ModelHub XC社区提供模型
Model: AIML-TUDA/LlavaGuard-v1.2-0.5B-OV-hf Source: Original Platform
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README.md
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---
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configs:
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- config_name: default
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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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base_model:
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- lmms-lab/llava-onevision-qwen2-0.5b-ov
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---
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## Model Summary
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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.
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- 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)
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- Repository: [ml-research/LlavaGuard](https://github.com/ml-research/LlavaGuard)
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- Project Website: [LlavaGuard](https://ml-research.github.io/human-centered-genai/projects/llavaguard/index.html)
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- Paper: [LlavaGuard-Arxiv](https://arxiv.org/abs/2406.05113)
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## Model Compatability
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- Inference: HF Tranformers✅, SGLang❌, LLaVA [repo](https://github.com/LLaVA-VL/LLaVA-NeXT)❌
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- Model Tuning:❌
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## Overview
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We here provide the transformers converted weights for LlavaGuard v1.2 0.5B.
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It builds upon LLaVA-OneVision 0.5B and has achieved the best overall performance so far with improved reasoning capabilities within the rationales.
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#### Usage
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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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```Python
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from transformers import AutoProcessor, LlavaOnevisionForConditionalGeneration
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from PIL import Image
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import requests
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model = LlavaOnevisionForConditionalGeneration.from_pretrained('AIML-TUDA/LlavaGuard-v1.2-0.5B-OV-hf')
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processor = AutoProcessor.from_pretrained('AIML-TUDA/LlavaGuard-v1.2-0.5B-OV-hf')
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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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text_prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
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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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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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## Safety Taxonomy
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Our default policy prompt looks like this:
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```Python
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prompt = """
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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". 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.
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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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## Citation
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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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crossref = { https://ml-research.github.io/human-centered-genai/projects/llavaguard/index.html },
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key = { Best Runner-Up Paper Award at NeurIPS RBFM 2024 },
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booktitle = { Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops and Working Notes of the NeurIPS 2024 Workshop on Responsibly Building the Next Generation of Multimodal Foundational Models (RBFM) },
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year = { 2024 },
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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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}
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```
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