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Model: AIML-TUDA/LlavaGuard-v1.2-0.5B-OV-hf
Source: Original Platform

.gitattributes

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README.md

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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, 
+            crossref = { https://ml-research.github.io/human-centered-genai/projects/llavaguard/index.html }, 
+            key = { Best Runner-Up Paper Award at NeurIPS RBFM 2024 }, 
+            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) }, 
+            year = { 2024 }, 
+            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 }
+}
+```

added_tokens.json

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+{
+  "<image>": 151646,
+  "<video>": 151647,
+  "<|endoftext|>": 151643,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644
+}

chat_template.json

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+{
+  "chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n'}}{# Render all images first #}{% for content in message['content'] | selectattr('type', 'equalto', 'image') %}{{ '<image>\n' }}{% endfor %}{# Render all video then #}{% for content in message['content'] | selectattr('type', 'equalto', 'video') %}{{ '<video>\n' }}{% endfor %}{# Render all text next #}{% if message['role'] != 'assistant' %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{{ content['text'] }}{% endfor %}{% else %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{% generation %}{{ content['text'] }}{% endgeneration %}{% endfor %}{% endif %}{{'<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"
+}

config.json

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+{
+  "_name_or_path": "/tmp_2",
+  "architectures": [
+    "LlavaOnevisionForConditionalGeneration"
+  ],
+  "image_grid_pinpoints": [
+    [
+      384,
+      384
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+    [
+      384,
+      768
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+      1152
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+  "image_token_index": 151646,
+  "model_type": "llava_onevision",
+  "projector_hidden_act": "gelu",
+  "text_config": {
+    "_name_or_path": "Qwen/Qwen2-0.5B-Instruct",
+    "architectures": [
+      "Qwen2ForCausalLM"
+    ],
+    "bos_token_id": 151643,
+    "eos_token_id": 151645,
+    "hidden_size": 896,
+    "intermediate_size": 4864,
+    "max_window_layers": 24,
+    "model_type": "qwen2",
+    "num_attention_heads": 14,
+    "num_hidden_layers": 24,
+    "num_key_value_heads": 2,
+    "rope_theta": 1000000.0,
+    "tie_word_embeddings": true,
+    "torch_dtype": "bfloat16",
+    "vocab_size": 152000
+  },
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.46.0",
+  "use_image_newline_parameter": true,
+  "video_token_index": 151647,
+  "vision_aspect_ratio": "anyres_max_9",
+  "vision_config": {
+    "hidden_size": 1152,
+    "image_size": 384,
+    "intermediate_size": 4304,
+    "model_type": "siglip_vision_model",
+    "num_attention_heads": 16,
+    "num_hidden_layers": 26,
+    "patch_size": 14,
+    "vision_use_head": false
+  },
+  "vision_feature_layer": -1,
+  "vision_feature_select_strategy": "full"
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "transformers_version": "4.46.0"
+}

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preprocessor_config.json

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+{
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_pad": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_grid_pinpoints": [
+    [
+      384,
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+  ],
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "LlavaOnevisionImageProcessor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "processor_class": "LlavaOnevisionProcessor",
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 384,
+    "width": 384
+  }
+}

processor_config.json

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+{
+  "image_token": "<image>",
+  "num_image_tokens": 729,
+  "processor_class": "LlavaOnevisionProcessor",
+  "video_token": "<video>",
+  "vision_feature_select_strategy": "full"
+}

special_tokens_map.json

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+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>"
+  ],
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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tokenizer_config.json

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+  "add_prefix_space": false,
+  "added_tokens_decoder": {
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+      "lstrip": false,
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+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>"
+  ],
+  "bos_token": null,
+  "chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "model_max_length": 32768,
+  "pad_token": "<|endoftext|>",
+  "processor_class": "LlavaOnevisionProcessor",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

video_processor/preprocessor_config.json

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+{
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "LlavaOnevisionVideoProcessor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "processor_class": "SiglipProcessor",
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 384,
+    "width": 384
+  }
+}