--- license: cc-by-nc-sa-4.0 language: - en tags: - instruct - veterinary - medicine - reasoning - healthcare - veterinary-ai - clinical-decision-support - veterinary-medicine - Animal Health - heathcare - medical - clinical - Vetmed - lifescience - Pharmaceutical - Vet Pharma - Enterprise LLM - Enterprise - Enterprise ready - veterinary-radiology - radiology - specialized-veterinary-ai - iso-42001-certified - hybrid-architecture - 7b-parameters - enterprise-ai extra_gated_heading: Access viggoVet Veterinary AI models on Hugging Face extra_gated_prompt: >- To access viggoVet models on Hugging Face, you're required to fill the request form in [viggoVet Veterinary AI page](https://viggo.vet/veterinary-ai/) Requests are subject to approval. extra_gated_button_content: Submit & Acknowledge license pipeline_tag: text-generation library_name: transformers --- # viggoVet-Radio-h 📡 ## 📋 Table of Contents - [Model Overview](#model-overview) - [Key Features](#key-features) - [Target Audience](#target-audience) - [Model Architecture](#model-architecture) - [Training Details](#training-details) - [Usage](#usage) - [Inference Examples](#inference-examples) - [Limitations](#limitations) - [Ethical Considerations](#ethical-considerations) - [Citation](#citation) - [License](#license) --- ## 🔬 Model Overview viggoVet-Radio-h is a specialized 7-billion parameter veterinary artificial intelligence model purpose-built for veterinary radiology applications. Built on a cutting-edge hybrid architecture and certified under ISO/IEC 42001:2023 enterprise standards, this model represents a significant advancement in specialized veterinary clinical decision support. This model leverages a sophisticated hybrid approach that combines advanced reasoning capabilities with domain-specific veterinary radiology knowledge, enabling it to handle complex clinical scenarios with unprecedented accuracy and reliability. The 7B parameter architecture strikes an optimal balance between computational efficiency and clinical performance, making it suitable for both research institutions and clinical practice environments. **Model Repository**: `viggovet/RadioVet-h` **Parameters**: 7 Billion **Specialty**: Veterinary Radiology **Architecture**: Hybrid (Sequential State Model + Transformer) **Certification**: ISO/IEC 42001:2023 **License**: CC BY-NC-SA 4.0 --- ## ⭐ Key Features - **🏗️ Advanced Hybrid Architecture**: Cutting-edge hybrid design optimized for veterinary radiology reasoning and clinical decision-making, delivering superior efficiency over traditional transformer-only models - **📊 7B Parameters**: Strategically architected with 7 billion parameters, achieving optimal performance-to-resource ratio for enterprise deployment - **🏢 ISO/IEC 42001:2023 Certified**: Fully compliant with the international standard for AI Management Systems (AIMS), ensuring accountability, explainability, data privacy, and reliability - **🎯 Veterinary Radiology Specialization**: Deep domain expertise with comprehensive clinical knowledge specific to veterinary radiology - **🧠 Enhanced Reasoning Capabilities**: Sophisticated multi-step reasoning for complex diagnostic workflows and treatment planning - **⚡ Superior Inference Efficiency**: Hybrid architecture reduces memory requirements by up to 70% compared to conventional models, enabling deployment on standard hardware - **🔒 Enterprise-Grade Security**: Built with clinical safety protocols, cryptographic signing, and comprehensive governance frameworks - **📈 Linear Scalability**: Memory scales linearly with context length, enabling efficient processing of long case histories and extensive medical records - **🌐 Production-Optimized**: Battle-tested architecture suitable for real-world veterinary practice environments and clinical integration - **✅ Regulatory-Ready**: Designed for deployment in highly regulated veterinary and healthcare environments --- ## 👥 Target Audience - **Veterinary Radiology Specialists**: Board-certified specialists seeking AI-powered clinical decision support - **Veterinary Teaching Hospitals**: Academic institutions requiring advanced veterinary radiology training and research tools - **Referral & Specialty Practices**: Multi-specialty practices with dedicated veterinary radiology departments - **Veterinary Researchers**: Scientists conducting veterinary radiology-focused research and clinical studies - **Clinical Software Developers**: Teams building veterinary radiology diagnostic platforms and management systems - **Enterprise Healthcare Organizations**: Large-scale veterinary enterprises requiring ISO/IEC 42001:2023-compliant AI solutions --- ## 🏗️ Model Architecture ### Hybrid Architecture Innovation viggoVet-Radio-h employs a sophisticated hybrid architecture that represents a fundamental advancement in enterprise AI design: **Core Architectural Components:** - **Hybrid Sequential-Transformer Design**: Combines linear-scaling sequence state models with strategic transformer layers for optimal efficiency and accuracy - **7B Parameter Optimization**: Precisely allocated 7 billion parameters for maximum clinical reasoning capability while maintaining computational efficiency - **Memory-Efficient Processing**: Achieves up to 70% reduction in memory requirements compared to transformer-only architectures - **Linear Context Scaling**: Memory and computation scale linearly with input length, enabling processing of extensive case histories without quadratic overhead - **Domain-Optimized Layers**: Specialized architectural components fine-tuned for veterinary medical reasoning and veterinary radiology clinical workflows - **Multi-Task Architecture**: Simultaneous support for diagnosis, treatment planning, clinical documentation, and case analysis ### ISO/IEC 42001:2023 Certification This model is certified under **ISO/IEC 42001:2023**, the world's first international standard for Artificial Intelligence Management Systems (AIMS). This certification ensures: - **Governance & Accountability**: Comprehensive AI governance frameworks with clear accountability structures - **Risk Management**: Enterprise-grade risk assessment and mitigation protocols - **Reliability & Robustness**: Rigorous testing for consistency and stability in clinical environments - **Ethical AI Framework**: Adherence to international AI ethics standards for healthcare applications - **Security & Privacy**: Enterprise-level data protection and privacy safeguards - **Transparency & Explainability**: Clear documentation of capabilities, limitations, and appropriate use cases ### Technical Specifications | Specification | Details | |--------------|---------| | **Parameters** | 7 Billion | | **Architecture Type** | Hybrid (Sequential State Model + Transformer) | | **Context Window** | Extended context for comprehensive case analysis | | **Memory Efficiency** | Up to 70% reduction vs. conventional architectures | | **Precision Support** | Mixed-precision for efficient deployment | | **Inference Optimization** | Production-optimized with linear scaling | | **Certification** | ISO/IEC 42001:2023 | | **License** | CC BY-NC-SA 4.0 | --- ## 📚 Training Details The model was developed using a rigorous, multi-phase training approach: 1. **Foundational Veterinary Knowledge**: Extensive training on peer-reviewed veterinary medical literature and clinical guidelines 2. **Veterinary Radiology Specialization**: Targeted training on veterinary radiology-specific cases, research, and protocols 3. **Clinical Reasoning Enhancement**: Advanced reasoning training for diagnostic and therapeutic decision-making 4. **Safety Alignment**: Rigorous alignment with veterinary clinical safety standards and ethical guidelines 5. **Enterprise Optimization**: Fine-tuning for reliability, consistency, and production deployment aligned with ISO/IEC 42001:2023 The training methodology emphasizes clinical accuracy, reasoning depth, and practical applicability in veterinary radiology practice settings. --- ## 💻 Usage ### Installation ```bash pip install transformers torch accelerate ``` ### Basic Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch # Load model and tokenizer model_name = "viggovet/RadioVet-h" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, device_map="auto", torch_dtype=torch.bfloat16 ) # Example veterinary radiology consultation prompt = """You are a board-certified veterinary radiology specialist. Analyze this clinical case and provide comprehensive decision support. Patient: 8-year-old Golden Retriever, Male Neutered Chief Complaint: Presenting for veterinary radiology evaluation Please provide: 1. Clinical assessment 2. Differential diagnoses 3. Recommended diagnostic workup 4. Treatment considerations """ inputs = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate( **inputs, max_new_tokens=2048, temperature=0.7, top_p=0.9, do_sample=True ) response = tokenizer.decode(outputs[0], skip_special_tokens=True) print(response) ``` --- ## 🔍 Inference Examples ### Example 1: Clinical Case Analysis ```python case_prompt = """ Analyze this veterinary radiology case: Species: Canine Breed: Labrador Retriever Age: 6 years Sex: Female Spayed Provide comprehensive veterinary radiology analysis including differential diagnoses, diagnostic recommendations, and treatment options. """ inputs = tokenizer(case_prompt, return_tensors="pt").to(model.device) response = model.generate(**inputs, max_new_tokens=1500, temperature=0.7) print(tokenizer.decode(response[0], skip_special_tokens=True)) ``` ### Example 2: Treatment Planning ```python treatment_query = """ For a patient diagnosed with a veterinary radiology condition, provide evidence-based treatment recommendations, monitoring protocols, and prognosis considerations. """ inputs = tokenizer(treatment_query, return_tensors="pt").to(model.device) plan = model.generate(**inputs, max_new_tokens=1200, temperature=0.7) print(tokenizer.decode(plan[0], skip_special_tokens=True)) ``` ### Example 3: Differential Diagnosis ```python ddx_prompt = """ Generate prioritized differential diagnoses for veterinary radiology clinical signs in a 10-year-old domestic shorthair cat, including key distinguishing features and recommended diagnostic tests. """ inputs = tokenizer(ddx_prompt, return_tensors="pt").to(model.device) differentials = model.generate(**inputs, max_new_tokens=1000, temperature=0.6) print(tokenizer.decode(differentials[0], skip_special_tokens=True)) ``` --- ## ⚠️ Limitations ### Clinical Limitations - **Not a Replacement for Veterinary Judgment**: This model is a clinical decision support tool and does NOT replace professional veterinary radiology expertise - **Requires Clinical Validation**: All recommendations must be validated by licensed veterinary professionals - **Species & Breed Variations**: Performance may vary across different animal species and breeds - **Emergency Situations**: Not designed for real-time emergency decision-making without veterinary oversight - **Knowledge Currency**: Medical knowledge evolves; users should supplement with current literature and guidelines ### Technical Limitations - **Language**: Primarily trained on English-language veterinary medical content - **Context Length**: Limited by context window for extremely lengthy case histories - **Modality**: Text-based model; does not directly process images or other visual diagnostics - **Rare Conditions**: Reduced accuracy for extremely rare veterinary radiology conditions ### Regulatory Considerations - Designed for clinical decision support and research purposes - Users must comply with local veterinary practice regulations - Licensed veterinarians remain legally responsible for all clinical decisions - Enterprise deployments should follow ISO/IEC 42001:2023 governance frameworks --- ## 🤝 Ethical Considerations ### Clinical Safety & Ethics - **Veterinary Oversight Required**: All clinical applications must be supervised by licensed veterinarians - **Bias Mitigation**: Trained to minimize species, breed, and geographic biases - **Transparency**: Clear documentation of capabilities and limitations - **Privacy**: Designed for use within veterinary data protection frameworks ### Responsible AI Principles - **Beneficence**: Optimized to support improved animal health outcomes in veterinary radiology - **Non-maleficence**: Safety protocols to minimize risk of harm from incorrect recommendations - **Autonomy**: Supports veterinary professional decision-making without replacing clinical judgment - **Justice**: Designed to provide equitable veterinary radiology support across diverse practice settings ### ISO/IEC 42001:2023 Compliance As an ISO/IEC 42001:2023 certified model, this system adheres to: - Comprehensive AI governance and risk management frameworks - Documented accountability and oversight mechanisms - Regular performance monitoring and validation protocols - Ethical AI development and deployment standards --- ## 📖 Citation If you use this model in your research or clinical practice, please cite: ```bibtex @misc{viggovetradioh2024, title={viggoVet-Radio-h: ISO/IEC 42001:2023 Certified Veterinary Radiology AI}, author={viggoVet Team}, year={2024}, publisher={Hugging Face}, howpublished={\url{https://huggingface.co/viggovet/RadioVet-h}}, note={7B parameter hybrid architecture model certified under ISO/IEC 42001:2023} } ``` --- ## 📄 License This model is released under the **Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)**. ### Key Terms: - ✅ **Attribution**: Credit must be given to viggoVet - ❌ **Non-Commercial**: Commercial use requires separate licensing - 🔄 **ShareAlike**: Derivative works must use the same license - 📧 **Commercial Licensing**: Contact viggo.vet for commercial licensing options For full license details, see [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). --- ## 🏢 About viggoVet viggoVet is committed to advancing veterinary medicine through responsible AI innovation. Our specialized models are designed to support veterinary professionals with cutting-edge clinical decision support tools while maintaining the highest standards of safety, ethics, and regulatory compliance. **Learn More**: [viggo.vet](https://viggo.vet) **Contact**: For enterprise licensing and support, visit [viggo.vet/veterinary-ai](https://viggo.vet/veterinary-ai/) --- **⚕️ Veterinary Professional Use Only**: This model is intended for use by licensed veterinary professionals and authorized personnel in veterinary medicine settings. Always consult with appropriate specialists for clinical decision-making.