Best AI tools for< Vet Candidates >

RightMatch is an AI-powered assessment tool designed to help hiring managers qualify and vet candidates more accurately and quickly. It uses AI-generated, skill-specific audio questions to assess candidates' tech skills in just 20-35 minutes, saving hiring teams up to 5 hours per hire. RightMatch also provides skill proficiency scores, transcripts, voice notes, and video recordings to help hiring teams make more informed decisions. Additionally, RightMatch is designed to minimize bias by focusing solely on skills, experience, and qualifications relevant to the job, and it provides actionable insights for candidates who may not be an exact match for a specific role.

YouTeam is an AI-powered platform that offers a transparent vetting process for hiring engineers. Leveraging deep learning technology, YouTeam provides customized vetting criteria tailored to each role's responsibilities and required skillset. The platform streamlines the hiring process by presenting engineering candidates aligned with the company's unique standards, allowing for a final interview to select the perfect match. With features like technical skill review assessments, coding assessments, and soft skills & culture fit questions, YouTeam ensures a data-rich candidate profile for informed decision-making.

The website 'Ask AI Vet' offers a free online service where users can ask veterinary questions and receive answers within 5 minutes through advanced artificial intelligence technology. Users can input their questions in a provided box and get detailed responses quickly. The platform is designed to provide general recommendations and preliminary evaluations for pet owners seeking advice on their animals' health and well-being.

PetsApp is a comprehensive veterinary engagement and communication platform that offers a range of features including text and SMS chat, appointment booking, reminders, digital payments, video calls, and AI-assisted communication. It is designed to streamline communication between veterinary teams and clients, improve client engagement, and enhance overall clinic efficiency. PetsApp has been widely adopted by veterinary professionals and clinics, helping them save time, increase client satisfaction, and boost wellness plan subscriptions.

Gushwork is an AI-assisted SEO tool designed to help businesses scale their online presence by combining human expertise with AI technology. The platform offers services such as AI-powered keyword and topic mapping, real human marketers to vet content, and automated organic presence building. Gushwork aims to increase website traffic, organic sales, and overall online visibility through data-driven strategies and technical SEO optimizations. The tool is purpose-built for consumer brands, SaaS, B2B services, and local consumer services, with a focus on integration and partnerships for seamless operations.

NotedSource is a global research and innovation platform that connects users to a network of research experts. The platform utilizes AI to scout, vet, and manage collaboration projects efficiently. Users can post requests to evaluate experts, startups, and technologies, streamline contract drafting, simplify payments, and access a single project management platform. NotedSource also offers learning and development solutions, executive education, and strategy and innovation services.

Ruru is an AI assistant application designed to provide quick and convenient answers to all your pet-related questions. It offers a cost-effective solution to pet owners by saving money on vet bills and ensuring the health of furry friends. With simple pricing and no hidden fees, Ruru is available 24/7 to assist you from the comfort of your home or on the go.

This solution provides a low-code ReactJS application to prototype and vet business use cases for GenAI using Retrieval Augmented Generation (RAG). It includes a backend Flask application that uses LangChain to provide PDF data as embeddings to a text-gen model via Amazon Bedrock and a vector database with FAISS or Kendra Index. The solution utilizes Amazon Bedrock as the only cost-generating AWS service.

ShapeLLM is the first 3D Multimodal Large Language Model designed for embodied interaction, exploring a universal 3D object understanding with 3D point clouds and languages. It supports single-view colored point cloud input and introduces a robust 3D QA benchmark, 3D MM-Vet, encompassing various variants. The model extends the powerful point encoder architecture, ReCon++, achieving state-of-the-art performance across a range of representation learning tasks. ShapeLLM can be used for tasks such as training, zero-shot understanding, visual grounding, few-shot learning, and zero-shot learning on 3D MM-Vet.

Hof is a CLI tool that unifies data models, schemas, code generation, and a task engine. It allows users to augment data, config, and schemas with CUE to improve consistency, generate multiple Yaml and JSON files, explore data or config with a TUI, and run workflows with automatic task dependency inference. The tool uses CUE to power the DX and implementation, providing a language for specifying schemas, configuration, and writing declarative code. Hof offers core features like code generation, data model management, task engine, CUE cmds, creators, modules, TUI, and chat for better, scalable results.

OpenKF (Open Knowledge Flow) is an online intelligent customer service system. It is an open-source customer service system based on OpenIM, supporting LLM (Local Knowledgebase) customer service and multi-channel customer service. It is easy to integrate with third-party systems, deploy, and perform secondary development. The system provides features like login page, config page, dashboard page, platform page, and session page. Users can quickly get started with OpenKF by following the installation and run instructions. The architecture follows MVC design with a standardized directory structure. The community encourages involvement through community meetings, contributions, and development. OpenKF is licensed under the Apache 2.0 license.

EVE is an official PyTorch implementation of Unveiling Encoder-Free Vision-Language Models. The project aims to explore the removal of vision encoders from Vision-Language Models (VLMs) and transfer LLMs to encoder-free VLMs efficiently. It also focuses on bridging the performance gap between encoder-free and encoder-based VLMs. EVE offers a superior capability with arbitrary image aspect ratio, data efficiency by utilizing publicly available data for pre-training, and training efficiency with a transparent and practical strategy for developing a pure decoder-only architecture across modalities.

VLMEvalKit is an open-source evaluation toolkit of large vision-language models (LVLMs). It enables one-command evaluation of LVLMs on various benchmarks, without the heavy workload of data preparation under multiple repositories. In VLMEvalKit, we adopt generation-based evaluation for all LVLMs, and provide the evaluation results obtained with both exact matching and LLM-based answer extraction.

Recent LLM (Large Language Models)-based CV and multi-modal works.

InternLM-XComposer2 is a groundbreaking vision-language large model (VLLM) based on InternLM2-7B excelling in free-form text-image composition and comprehension. It boasts several amazing capabilities and applications: * **Free-form Interleaved Text-Image Composition** : InternLM-XComposer2 can effortlessly generate coherent and contextual articles with interleaved images following diverse inputs like outlines, detailed text requirements and reference images, enabling highly customizable content creation. * **Accurate Vision-language Problem-solving** : InternLM-XComposer2 accurately handles diverse and challenging vision-language Q&A tasks based on free-form instructions, excelling in recognition, perception, detailed captioning, visual reasoning, and more. * **Awesome performance** : InternLM-XComposer2 based on InternLM2-7B not only significantly outperforms existing open-source multimodal models in 13 benchmarks but also **matches or even surpasses GPT-4V and Gemini Pro in 6 benchmarks** We release InternLM-XComposer2 series in three versions: * **InternLM-XComposer2-4KHD-7B** 🤗: The high-resolution multi-task trained VLLM model with InternLM-7B as the initialization of the LLM for _High-resolution understanding_ , _VL benchmarks_ and _AI assistant_. * **InternLM-XComposer2-VL-7B** 🤗 : The multi-task trained VLLM model with InternLM-7B as the initialization of the LLM for _VL benchmarks_ and _AI assistant_. **It ranks as the most powerful vision-language model based on 7B-parameter level LLMs, leading across 13 benchmarks.** * **InternLM-XComposer2-VL-1.8B** 🤗 : A lightweight version of InternLM-XComposer2-VL based on InternLM-1.8B. * **InternLM-XComposer2-7B** 🤗: The further instruction tuned VLLM for _Interleaved Text-Image Composition_ with free-form inputs. Please refer to Technical Report and 4KHD Technical Reportfor more details.

SeaLLMs are a family of language models optimized for Southeast Asian (SEA) languages. They were pre-trained from Llama-2, on a tailored publicly-available dataset, which comprises texts in Vietnamese 🇻🇳, Indonesian 🇮🇩, Thai 🇹🇭, Malay 🇲🇾, Khmer🇰🇭, Lao🇱🇦, Tagalog🇵🇭 and Burmese🇲🇲. The SeaLLM-chat underwent supervised finetuning (SFT) and specialized self-preferencing DPO using a mix of public instruction data and a small number of queries used by SEA language native speakers in natural settings, which **adapt to the local cultural norms, customs, styles and laws in these areas**. SeaLLM-13b models exhibit superior performance across a wide spectrum of linguistic tasks and assistant-style instruction-following capabilities relative to comparable open-source models. Moreover, they outperform **ChatGPT-3.5** in non-Latin languages, such as Thai, Khmer, Lao, and Burmese.

Uni-MoE is a MoE-based unified multimodal model that can handle diverse modalities including audio, speech, image, text, and video. The project focuses on scaling Unified Multimodal LLMs with a Mixture of Experts framework. It offers enhanced functionality for training across multiple nodes and GPUs, as well as parallel processing at both the expert and modality levels. The model architecture involves three training stages: building connectors for multimodal understanding, developing modality-specific experts, and incorporating multiple trained experts into LLMs using the LoRA technique on mixed multimodal data. The tool provides instructions for installation, weights organization, inference, training, and evaluation on various datasets.

I'm your coach for benefits filing!

A supportive companion for veterans seeking mental health guidance.

I can help you with any questions you have about your pet.

Master Animal Health Fundamentals Using Games, Quizzes, Case Studies, Interviews & Role-Plays

Guiding veterans on PACT Act benefits, VA healthcare, military justice, and education, tailored for veterans and their families.

Gruff WWII vet Grandpa, straight-talking and unfiltered.

Personal assistant to Let's Adopt International. Ask me anything about animal rescue, vet sciences and Let's Adopt

Assists with pet emergencies using photo sharing for web-based solutions.

🐈‍⬛️ Your friendly pet health expert.

Nahbarer Freund in Sachen Tiergesundheit, nutzt lockerere & selbstbewusste Tonalität.

A Garfield-themed cat care advisor.

Your personal assistant for new dog owner advice.

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