Upload LlavaOnevisionForConditionalGeneration

README.md

@@ -1,194 +1,199 @@
 ---
-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-qwen2-7b-ov
+library_name: transformers
+tags: []
 ---
 
+# Model Card for Model ID
 
+<!-- Provide a quick summary of what the model is/does. -->
 
-## Model Summary
-LlavaGuard-v1.2-7B-OV is trained on [LlavaGuard-DS](https://huggingface.co/datasets/AIML-TUDA/LlavaGuard) and based on llava-onevision-qwen2-7b-ov model with a context window of 32K tokens.
-
-- Links to Model Versions: [sglang](https://huggingface.co/datasets/AIML-TUDA/LlavaGuard-v1.2-7B-OV), [tranformers](https://huggingface.co/datasets/AIML-TUDA/LlavaGuard-v1.2-7B-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: SGLang❌, LLaVA [repo](https://github.com/LLaVA-VL/LLaVA-NeXT)❌, HF Tranformers✅
-- Model Tuning:❌
-
-## Overview
-We here provide the transformers converted weights for LlavaGuard v1.2 7B.
-It builds upon LLaVA-OneVision 7B and has achieved the best overall performance so far with improved reasoning capabilities within the rationales.
-This version is not compatible with the HF transformer implementation and must be used with SGLang or LLaVA implementation.
-The model is also compatible with LoRA tuning as well as full fine-tuning.
-For tuning, you can adopt and use the training scripts provided in our repository (see [ml-research/LlavaGuard](https://github.com/ml-research/LlavaGuard)).
-A suitable docker image can be found at our Github repo, too.
-
-#### Usage
-
-# 0. Install requirements
-For inference, you use the following [sglang docker](https://github.com/sgl-project/sglang/blob/main/docker/Dockerfile) and proceed with step 1.
-Otherwise, you can also install sglang via pip or from source [see here](https://github.com/sgl-project/sglang).
-
-# 1. Select a model and start an SGLang server
-
-    CUDA_VISIBLE_DEVICES=0 python3 -m sglang.launch_server --model-path AIML-TUDA/LlavaGuard-v1.2-7B-OV --port 10000
-   
-# 2. Model Inference
-For model inference, you can access this server by running the code provided below, e.g. 
-`python my_script.py`
-
-```Python
-import sglang as sgl
-from sglang import RuntimeEndpoint
-
-@sgl.function
-def guard_gen(s, image_path, prompt):
-    s += sgl.user(sgl.image(image_path) + prompt)
-    hyperparameters = {
-        'temperature': 0.2,
-        'top_p': 0.95,
-        'top_k': 50,
-        'max_tokens': 500,
-    }
-    s += sgl.assistant(sgl.gen("json_output", **hyperparameters))
-
-im_path = 'path/to/your/image'
-prompt = safety_taxonomy_below
-backend = RuntimeEndpoint(f"http://localhost:10000")
-sgl.set_default_backend(backend)
-out = guard_gen.run(image_path=im_path, prompt=prompt)
-print(out['json_output'])
-```
-
-## 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 },
-}
-```
+
+
+## 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.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- 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. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+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).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
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+
+#### Software
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+
+## Model Card Authors [optional]
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+
+## Model Card Contact
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+[More Information Needed]

config.json

@@ -0,0 +1,190 @@
+{
+  "_name_or_path": "/tmp-model/",
+  "architectures": [
+    "LlavaOnevisionForConditionalGeneration"
+  ],
+  "image_grid_pinpoints": [
+    [
+      384,
+      384
+    ],
+    [
+      384,
+      768
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+      1152
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+      1536
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+    [
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+  ],
+  "image_token_index": 151646,
+  "model_type": "llava_onevision",
+  "projector_hidden_act": "gelu",
+  "text_config": {
+    "_name_or_path": "Qwen/Qwen2-7B-Instruct",
+    "architectures": [
+      "Qwen2ForCausalLM"
+    ],
+    "bos_token_id": 151643,
+    "eos_token_id": 151645,
+    "hidden_size": 3584,
+    "intermediate_size": 18944,
+    "model_type": "qwen2",
+    "num_attention_heads": 28,
+    "num_hidden_layers": 28,
+    "num_key_value_heads": 4,
+    "rope_theta": 1000000.0,
+    "torch_dtype": "bfloat16",
+    "vocab_size": 152128
+  },
+  "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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