Alternatives to Falcon-40B

Instruction-following models like GPT-3.5 (text-DaVinci-003), ChatGPT, Claude, and Bing Chat have seen significant advancements in their capabilities, leading to a rise in their usage among individuals in both personal and professional contexts. Despite their growing popularity and integration into daily tasks, these models are not without their shortcomings, as they can sometimes disseminate inaccurate information, reinforce harmful stereotypes, and use inappropriate language. To effectively tackle these critical issues, it is essential for researchers and scholars to become actively involved in exploring these models further. However, conducting research on instruction-following models within academic settings has posed challenges due to the unavailability of models with comparable functionality to proprietary options like OpenAI’s text-DaVinci-003. In response to this gap, we are presenting our insights on an instruction-following language model named Alpaca, which has been fine-tuned from Meta’s LLaMA 7B model, aiming to contribute to the discourse and development in this field. This initiative represents a step towards enhancing the understanding and capabilities of instruction-following models in a more accessible manner for researchers.

CrowdStrike Falcon is a cutting-edge cybersecurity platform that operates in the cloud, delivering robust defenses against a variety of cyber threats such as malware, ransomware, and complex attacks. By utilizing artificial intelligence and machine learning technologies, it enables real-time detection and response to potential security incidents, while offering features like endpoint protection, threat intelligence, and incident response. The system employs a lightweight agent that consistently scans endpoints for any indicators of malicious behavior, ensuring visibility and security with minimal effect on overall system performance. Falcon's cloud-based framework facilitates quick updates, adaptability, and swift threat responses across extensive and distributed networks. Its extensive suite of security functionalities empowers organizations to proactively prevent, identify, and address cyber risks, establishing it as an essential resource for contemporary enterprise cybersecurity. Additionally, its seamless integration with existing infrastructures enhances overall security posture while minimizing operational disruptions.

Magic’s LTM-1 technology facilitates context windows that are 50 times larger than those typically used in transformer models. As a result, Magic has developed a Large Language Model (LLM) that can effectively process vast amounts of contextual information when providing suggestions. This advancement allows our coding assistant to access and analyze your complete code repository. With the ability to reference extensive factual details and their own prior actions, larger context windows can significantly enhance the reliability and coherence of AI outputs. We are excited about the potential of this research to further improve user experience in coding assistance applications.

Falcon-7B is a causal decoder-only model comprising 7 billion parameters, developed by TII and trained on an extensive dataset of 1,500 billion tokens from RefinedWeb, supplemented with specially selected corpora, and it is licensed under Apache 2.0. What are the advantages of utilizing Falcon-7B? This model surpasses similar open-source alternatives, such as MPT-7B, StableLM, and RedPajama, due to its training on a remarkably large dataset of 1,500 billion tokens from RefinedWeb, which is further enhanced with carefully curated content, as evidenced by its standing on the OpenLLM Leaderboard. Additionally, it boasts an architecture that is finely tuned for efficient inference, incorporating technologies like FlashAttention and multiquery mechanisms. Moreover, the permissive nature of the Apache 2.0 license means users can engage in commercial applications without incurring royalties or facing significant limitations. This combination of performance and flexibility makes Falcon-7B a strong choice for developers seeking advanced modeling capabilities.

We are excited to present MPT-7B, the newest addition to the MosaicML Foundation Series. This transformer model has been meticulously trained from the ground up using 1 trillion tokens of diverse text and code. It is open-source and ready for commercial applications, delivering performance on par with LLaMA-7B. The training process took 9.5 days on the MosaicML platform, requiring no human input and incurring an approximate cost of $200,000. With MPT-7B, you can now train, fine-tune, and launch your own customized MPT models, whether you choose to begin with one of our provided checkpoints or start anew. To provide additional options, we are also introducing three fine-tuned variants alongside the base MPT-7B: MPT-7B-Instruct, MPT-7B-Chat, and MPT-7B-StoryWriter-65k+, the latter boasting an impressive context length of 65,000 tokens, allowing for extensive content generation. These advancements open up new possibilities for developers and researchers looking to leverage the power of transformer models in their projects.

Introducing the next iteration of our open-source large language model, this version features model weights along with initial code for the pretrained and fine-tuned Llama language models, which span from 7 billion to 70 billion parameters. The Llama 2 pretrained models have been developed using an impressive 2 trillion tokens and offer double the context length compared to their predecessor, Llama 1. Furthermore, the fine-tuned models have been enhanced through the analysis of over 1 million human annotations. Llama 2 demonstrates superior performance against various other open-source language models across multiple external benchmarks, excelling in areas such as reasoning, coding capabilities, proficiency, and knowledge assessments. For its training, Llama 2 utilized publicly accessible online data sources, while the fine-tuned variant, Llama-2-chat, incorporates publicly available instruction datasets along with the aforementioned extensive human annotations. Our initiative enjoys strong support from a diverse array of global stakeholders who are enthusiastic about our open approach to AI, including companies that have provided valuable early feedback and are eager to collaborate using Llama 2. The excitement surrounding Llama 2 signifies a pivotal shift in how AI can be developed and utilized collectively.

Easily train and deploy large-scale AI models with just a single command by pointing to your S3 bucket—then let us take care of everything else, including orchestration, efficiency, node failures, and infrastructure management. The process is straightforward and scalable, allowing you to utilize MosaicML to train and serve large AI models using your own data within your secure environment. Stay ahead of the curve with our up-to-date recipes, techniques, and foundation models, all developed and thoroughly tested by our dedicated research team. With only a few simple steps, you can deploy your models within your private cloud, ensuring that your data and models remain behind your own firewalls. You can initiate your project in one cloud provider and seamlessly transition to another without any disruptions. Gain ownership of the model trained on your data while being able to introspect and clarify the decisions made by the model. Customize content and data filtering to align with your business requirements, and enjoy effortless integration with your existing data pipelines, experiment trackers, and other essential tools. Our solution is designed to be fully interoperable, cloud-agnostic, and validated for enterprise use, ensuring reliability and flexibility for your organization. Additionally, the ease of use and the power of our platform allow teams to focus more on innovation rather than infrastructure management.

OpenLLaMA is an openly licensed reproduction of Meta AI's LLaMA 7B, developed using the RedPajama dataset. The model weights we offer can seamlessly replace the LLaMA 7B in current applications. Additionally, we have created a more compact 3B version of the LLaMA model for those seeking a lighter alternative. This provides users with more flexibility in choosing the right model for their specific needs.

Vicuna-13B is an open-source conversational agent developed through the fine-tuning of LLaMA, utilizing a dataset of user-shared dialogues gathered from ShareGPT. Initial assessments, with GPT-4 serving as an evaluator, indicate that Vicuna-13B achieves over 90% of the quality exhibited by OpenAI's ChatGPT and Google Bard, and it surpasses other models such as LLaMA and Stanford Alpaca in more than 90% of instances. The entire training process for Vicuna-13B incurs an estimated expenditure of approximately $300. Additionally, the source code and model weights, along with an interactive demonstration, are made available for public access under non-commercial terms, fostering a collaborative environment for further development and exploration. This openness encourages innovation and enables users to experiment with the model's capabilities in diverse applications.

Foundation models, including GPT-4, have significantly accelerated advancements in artificial intelligence, yet the most advanced models remain either proprietary or only partially accessible. In response to this challenge, the RedPajama initiative aims to develop a collection of top-tier, fully open-source models. We are thrilled to announce that we have successfully completed the initial phase of this endeavor: recreating the LLaMA training dataset, which contains over 1.2 trillion tokens. Currently, many of the leading foundation models are locked behind commercial APIs, restricting opportunities for research, customization, and application with sensitive information. The development of fully open-source models represents a potential solution to these limitations, provided that the open-source community can bridge the gap in quality between open and closed models. Recent advancements have shown promising progress in this area, suggesting that the AI field is experiencing a transformative period akin to the emergence of Linux. The success of Stable Diffusion serves as a testament to the fact that open-source alternatives can not only match the quality of commercial products like DALL-E but also inspire remarkable creativity through the collaborative efforts of diverse communities. By fostering an open-source ecosystem, we can unlock new possibilities for innovation and ensure broader access to cutting-edge AI technology.

Falcon Mamba 7B marks a significant milestone as the inaugural open-source State Space Language Model (SSLM), presenting a revolutionary architecture within the Falcon model family. Celebrated as the premier open-source SSLM globally by Hugging Face, it establishes a new standard for efficiency in artificial intelligence. In contrast to conventional transformers, SSLMs require significantly less memory and can produce lengthy text sequences seamlessly without extra resource demands. Falcon Mamba 7B outperforms top transformer models, such as Meta’s Llama 3.1 8B and Mistral’s 7B, demonstrating enhanced capabilities. This breakthrough not only highlights Abu Dhabi’s dedication to pushing the boundaries of AI research but also positions the region as a pivotal player in the global AI landscape. Such advancements are vital for fostering innovation and collaboration in technology.

Falcon 2 11B is a versatile AI model that is open-source, supports multiple languages, and incorporates multimodal features, particularly excelling in vision-to-language tasks. It outperforms Meta’s Llama 3 8B and matches the capabilities of Google’s Gemma 7B, as validated by the Hugging Face Leaderboard. In the future, the development plan includes adopting a 'Mixture of Experts' strategy aimed at significantly improving the model's functionalities, thereby advancing the frontiers of AI technology even further. This evolution promises to deliver remarkable innovations, solidifying Falcon 2's position in the competitive landscape of artificial intelligence.

FalconTune© is an advanced software solution designed for the tuning of PID loops within distributed control systems (DCS) and programmable logic controllers (PLC). For those interested, a comprehensive overview of the software can be provided. While FalconTune© is specifically fine-tuned for Yokogawa DCS©, its compatibility extends to any DCS or PLC that utilizes the OPC communication protocol. Users benefit from various licensing options that cater to their needs, including yearly, perpetual, workplace-based, and site-wide licenses. The research and development team at IMB Controls Inc. focuses on innovations in process control. This company, founded by Dr. Igor Boiko—a prominent figure in control systems research—has launched FalconTune, an automated solution for PID loop tuning. Currently, Dr. Boiko holds a professorship in Electrical Engineering at the Petroleum Institute (PI) located in Abu Dhabi, where he continues to contribute to advancements in the field. The combination of cutting-edge technology and expert knowledge makes FalconTune a valuable tool for engineers and technicians alike.

Falcon 3 is a large language model that has been made open-source by the Technology Innovation Institute (TII), aiming to broaden access to advanced AI capabilities. Its design prioritizes efficiency, enabling it to function effectively on lightweight devices like laptops while maintaining high performance levels. The Falcon 3 suite includes four scalable models, each specifically designed for various applications and capable of supporting multiple languages while minimizing resource consumption. This new release in TII's LLM lineup sets a benchmark in reasoning, language comprehension, instruction adherence, coding, and mathematical problem-solving. By offering a blend of robust performance and resource efficiency, Falcon 3 seeks to democratize AI access, allowing users in numerous fields to harness sophisticated technology without the necessity for heavy computational power. Furthermore, this initiative not only enhances individual capabilities but also fosters innovation across different sectors by making advanced AI tools readily available.

The Mixtral 8x7B model is an advanced sparse mixture of experts (SMoE) system that boasts open weights and is released under the Apache 2.0 license. This model demonstrates superior performance compared to Llama 2 70B across various benchmarks while achieving inference speeds that are six times faster. Recognized as the leading open-weight model with a flexible licensing framework, Mixtral also excels in terms of cost-efficiency and performance. Notably, it competes with and often surpasses GPT-3.5 in numerous established benchmarks, highlighting its significance in the field. Its combination of accessibility, speed, and effectiveness makes it a compelling choice for developers seeking high-performing AI solutions.

Mistral 7B is a language model with 7.3 billion parameters that demonstrates superior performance compared to larger models such as Llama 2 13B on a variety of benchmarks. It utilizes innovative techniques like Grouped-Query Attention (GQA) for improved inference speed and Sliding Window Attention (SWA) to manage lengthy sequences efficiently. Released under the Apache 2.0 license, Mistral 7B is readily available for deployment on different platforms, including both local setups and prominent cloud services. Furthermore, a specialized variant known as Mistral 7B Instruct has shown remarkable capabilities in following instructions, outperforming competitors like Llama 2 13B Chat in specific tasks. This versatility makes Mistral 7B an attractive option for developers and researchers alike.

The Falcon LLM represents a cutting-edge open-source family of large language models created by the Technology Innovation Institute (TII), offering sophisticated generative AI functionalities for natural language comprehension, logical reasoning, and content creation across a wide array of applications. Constructed using hybrid Transformer and Mamba architectures, the Falcon models range from compact 7B versions that excel in reasoning tasks to the formidable Falcon 180B, which competes with top-tier AI systems, ensuring both efficiency and scalability for various real-world applications like text generation, translation, summarization, sentiment analysis, and interactive chat agents. Additionally, it accommodates multilingual and multimodal functionality, effectively managing diverse languages and data formats while incorporating features for long context processing, coding tasks, logic, and mathematical reasoning, all while maintaining performance in environments with limited resources. This versatility makes the Falcon LLM a valuable tool for developers and researchers exploring innovative AI solutions.

Falcon is an exceptionally swift and lightweight Python web API framework designed for creating powerful application backends and microservices. It seamlessly integrates with both asyncio (ASGI) and gevent/meinheld (WSGI), making it flexible for various use cases. The framework promotes the principles of REST architecture, allowing developers to implement resource classes that manage HTTP method handlers to address requests and facilitate state changes. By enhancing traditional Python web frameworks, Falcon delivers added reliability, adaptability, and efficiency tailored to your specific needs. Additionally, there is a variety of Falcon add-ons, templates, and supplementary packages available for integration into your projects. For a comprehensive start, several of these resources are compiled on the Falcon wiki, and exploring PyPI could uncover even more useful tools and libraries. This ecosystem of resources ensures developers have ample support as they build and scale their applications.

Falcon Forms allows you to digitize your day-today paperwork. Falcon Forms allows you to quickly create and configure forms that are tailored to your business or activity. Falcon Forms can be adapted to paper-based workflows. This app is offline and doesn't require any network connection to create templates or fill out forms. Falcon Forms can be used to provide reporting and documentation for almost any line of work, regardless of domain. Falcon Forms allows real-time collaboration. It allows sharing of PDF files and Form Templates in JSON format via email and third party cloud storage providers such as iCloud, Box and Dropbox, OneDrive, Adobe Creative Cloud and Google Drive.

Regardless of the number of initiatives, strategies, or concepts you manage in your business transformation, Falcon consolidates everything and provides you with the tools to plan, measure, and report effortlessly. The disorder you once faced is finally resolved! Falcon presents all your strategic metrics in a single view, ensuring that your strategy projects are no longer dispersed across various lists, emails, and documents. It seamlessly integrates all timelines, budgets, and status updates, automatically compiling them for your convenience. This way, you can maintain a comprehensive perspective and have confidence that your strategy is effectively executed. Collaborate with your team on essential initiatives without stress, in real-time, from any location. Break your strategic projects into manageable segments and assign responsibilities accordingly. Thanks to Falcon's intuitive tree structure, handling this intricate task becomes straightforward. With a wealth of templates available, you can swiftly prepare your implementation strategy. Additionally, you can monitor the milestones and financial implications of your strategic endeavors with ease, ensuring nothing falls through the cracks. This level of organization fosters accountability and enhances overall productivity within your team.

Fullmoon is an innovative, open-source application designed to allow users to engage directly with large language models on their personal devices, prioritizing privacy and enabling offline use. Tailored specifically for Apple silicon, it functions smoothly across various platforms, including iOS, iPadOS, macOS, and visionOS. Users have the ability to customize their experience by modifying themes, fonts, and system prompts, while the app also works seamlessly with Apple's Shortcuts to enhance user productivity. Notably, Fullmoon is compatible with models such as Llama-3.2-1B-Instruct-4bit and Llama-3.2-3B-Instruct-4bit, allowing for effective AI interactions without requiring internet connectivity. This makes it a versatile tool for anyone looking to harness the power of AI conveniently and privately.

FalconStor stands out as a reliable innovator in data protection, managing more than an Exabyte of data and empowering some of the globe's most challenging enterprises to upgrade their data backup and archival processes across both data centers and public cloud environments. The firm enhances data security and facilitates rapid recovery from ransomware incidents, all while reducing costs by as much as 90 percent. With a strong reputation, FalconStor serves a diverse clientele of 1,000 customers and collaborates with a wide-reaching network of partners worldwide. This commitment to excellence and innovation makes FalconStor a key player in the data management sector.

Introducing Mistral NeMo, our latest and most advanced small model yet, featuring a cutting-edge 12 billion parameters and an expansive context length of 128,000 tokens, all released under the Apache 2.0 license. Developed in partnership with NVIDIA, Mistral NeMo excels in reasoning, world knowledge, and coding proficiency within its category. Its architecture adheres to industry standards, making it user-friendly and a seamless alternative for systems currently utilizing Mistral 7B. To facilitate widespread adoption among researchers and businesses, we have made available both pre-trained base and instruction-tuned checkpoints under the same Apache license. Notably, Mistral NeMo incorporates quantization awareness, allowing for FP8 inference without compromising performance. The model is also tailored for diverse global applications, adept in function calling and boasting a substantial context window. When compared to Mistral 7B, Mistral NeMo significantly outperforms in understanding and executing detailed instructions, showcasing enhanced reasoning skills and the ability to manage complex multi-turn conversations. Moreover, its design positions it as a strong contender for multi-lingual tasks, ensuring versatility across various use cases.

Falcon X Recon reveals digital threats by surveilling the obscure corners of the internet where cybercriminals operate and illicit markets flourish. It offers immediate insights into potential dangers, streamlining investigation processes and enhancing overall response efficiency. By utilizing Falcon X Recon from Day One, organizations can swiftly combat digital risks without the need for installation, management, or deployment, as it is integrated into the cloud-native CrowdStrike Falcon® Platform. This tool helps identify various risks to business, reputation, and third-party relationships that may arise from compromised credentials, personal identifiable information (PII), and sensitive financial details. Users can monitor both current and past conversations and interactions to better understand adversarial behavior that might threaten their organization or its employees. Additionally, customizable dashboards empower users to easily access real-time notifications and delve deeper into critical alerts for more thorough analysis, ensuring a proactive stance against potential threats. By leveraging these insights, companies can fortify their defenses and maintain a secure environment for their operations and personnel.

CrowdStrike Falcon® Adversary Intelligence is a powerful tool for businesses looking to enhance their cybersecurity posture. Offering access to detailed adversary profiles and automated threat intelligence, it helps organizations understand who their attackers are and how to defend against them. The platform's advanced features, such as dark web monitoring, threat modeling, and sandbox analysis, provide critical insights and rapid response capabilities. With seamless integrations and automated workflows, Falcon® ensures that security teams can respond faster and more effectively to emerging cyber threats.

The effectiveness of transport planning, monitoring, settlement, and analysis has never been greater. TMS FALCON® is an IT system belonging to the TMS class, which is an expert at reducing transport management costs. Extended price lists allow you to select the cheapest carrier and negotiate prices. This is why you will calculate transport costs during the planning stage. You will be able monitor the delivery of goods with TMS FALCON®, and reduce downtime. TMS FALCON®, an advanced transport management system, contains many functionalities that allow for effective control over drivers and vehicle fleets as well as transport service providers.

Falcon Firewall Management offers a straightforward and centralized method for creating, managing, and enforcing security policies. It effectively safeguards against network threats while providing immediate visibility to improve protection and facilitate informed decision-making. Utilizing the same lightweight Falcon agent, management console, and cloud-native framework, it can be deployed and become operational within minutes. This system simplifies operations by maintaining a consistent architecture, eliminating the need for reboots, intricate configurations, or fine-tuning during deployment. By consolidating endpoint protection and host firewall management into a single management console, it streamlines workflows and enhances visibility across security controls. Additionally, it automatically detects and displays specific activities, potential threats, and network anomalies, ensuring that users can respond swiftly to emerging risks. With Falcon Firewall Management, organizations can maintain a robust security posture while minimizing administrative overhead.

Falcon Cloud Workload Protection offers comprehensive insight into events related to workloads and containers, along with instance metadata, facilitating quicker and more precise detection, response, threat hunting, and investigation, ensuring that every detail in your cloud infrastructure is accounted for. This solution safeguards your entire cloud-native ecosystem across all environments, covering every workload, container, and Kubernetes application. It automates security measures to identify and mitigate suspicious behavior, zero-day vulnerabilities, and high-risk actions, enabling you to proactively address threats and minimize your attack surface. Furthermore, Falcon Cloud Workload Protection features essential integrations that enhance continuous integration/continuous delivery (CI/CD) processes, empowering you to secure workloads rapidly in sync with DevOps without compromising performance. By leveraging these capabilities, organizations can maintain a robust security posture in an increasingly dynamic cloud landscape.

Falcon Spotlight offers immediate insight throughout your organization, equipping you with the essential and timely information necessary to minimize your risk of attacks without affecting your endpoints. This feature is part of a comprehensive platform designed to thwart exploits and mitigate post-exploit actions, enabling thorough research into common vulnerabilities and exposures (CVEs) while analyzing the profiles and targets of threat actors. Employing a scanless technology approach, Falcon Spotlight provides an automated vulnerability management solution that operates continuously, presenting prioritized information in real-time. It replaces outdated, cumbersome reports with a quick and user-friendly dashboard, enhancing accessibility and efficiency. The cloud-based CrowdStrike Falcon® platform, supported by a single lightweight agent, gathers data once for multiple reuse, streamlining the process. Consequently, Spotlight operates without the need for additional agents, hardware, scanners, or credentials; you simply activate it and begin using it immediately. This seamless integration ensures that organizations can respond swiftly to vulnerabilities as they arise.

Teuken-7B is a multilingual language model that has been developed as part of the OpenGPT-X initiative, specifically tailored to meet the needs of Europe's varied linguistic environment. This model has been trained on a dataset where over half consists of non-English texts, covering all 24 official languages of the European Union, which ensures it performs well across these languages. A significant advancement in Teuken-7B is its unique multilingual tokenizer, which has been fine-tuned for European languages, leading to enhanced training efficiency and lower inference costs when compared to conventional monolingual tokenizers. Users can access two versions of the model: Teuken-7B-Base, which serves as the basic pre-trained version, and Teuken-7B-Instruct, which has received instruction tuning aimed at boosting its ability to respond to user requests. Both models are readily available on Hugging Face, fostering an environment of transparency and collaboration within the artificial intelligence community while also encouraging further innovation. The creation of Teuken-7B highlights a dedication to developing AI solutions that embrace and represent the rich diversity found across Europe.

Falcon Forensics delivers an all-encompassing solution for data collection and triage analysis during investigative processes. The field of forensic security typically involves extensive searches utilizing a variety of tools. By consolidating your collection and analysis into a single solution, you can accelerate the triage process. This enables incident responders to act more swiftly during investigations while facilitating compromise assessments, threat hunting, and monitoring efforts with Falcon Forensics. With pre-built dashboards and user-friendly search and viewing capabilities, analysts can rapidly sift through extensive datasets, including historical records. Falcon Forensics streamlines the data collection process and offers in-depth insights regarding incidents. Responders can access comprehensive threat context without the need for protracted queries or complete disk image collections. This solution empowers incident responders to efficiently analyze large volumes of data, both in a historical context and in real-time, allowing them to uncover critical information essential for effective incident triage. Ultimately, Falcon Forensics enhances the overall investigation workflow, leading to quicker and more informed decision-making.

CrowdStrike Exposure Management is a platform for managing attack surfaces that provides 24/7 discovery of exposed assets in all environments, including the supply chain. CrowdStrike Falcon Exposure Management is used by leading enterprises around the world to gain unprecedented visibility of their internet facing assets and actionable insights for eliminating shadow IT risk. CrowdStrike's Falcon Exposure Management's proprietary mapping technology maps all internet-exposed assets in real time. Cutting-edge ML classification engines and association engines analyze and create your inventory automatically. CrowdStrike EASM is unique in its ability to prioritize risks based on adversary intelligence. Understanding threats from the attacker's point of view will help you secure your assets.

UVI Falcon is an all-encompassing hybrid instrument that merges sophisticated sampling and synthesis features within a semi-modular setup. It boasts 20 different oscillator types, such as virtual analog, wavetable, FM, additive, and granular synthesis, which opens up a vast array of sound design opportunities. Users can take advantage of more than 100 high-quality effects to apply various processing techniques like reverb, delay, modulation, and filtering, thereby elevating their audio creations. The modulation system in Falcon is robust, incorporating envelopes, LFOs, and step sequencers that allow for dynamic and evolving soundscapes. Additionally, the software facilitates MIDI processing and scripting, equipping users with the tools necessary for crafting intricate performances and custom instruments. Its user interface is flexible, featuring a redesigned browser for streamlined preset management along with an intuitive workflow that simplifies sound creation. Falcon also includes a factory library with over 1,500 presets, and provides expansion possibilities through supplemental sound libraries, ensuring an ever-evolving creative experience. Overall, UVI Falcon stands out as a versatile tool for musicians and sound designers alike.

Falcon Horizon offers ongoing agentless discovery and visibility of cloud-native resources, spanning from the host to the cloud, which equips users with essential context and insights to enhance their security stance and identify necessary actions to avert potential threats. This platform enables sophisticated agentless surveillance of cloud resources to identify misconfigurations, vulnerabilities, and security risks, while also providing guided remediation to tackle these issues, empowering developers with safeguards to prevent expensive errors. By employing an adversary-focused strategy, Falcon Horizon delivers real-time threat intelligence on over 150 adversary groups and 50 indicators of attack, coupled with remediation guidance that can accelerate investigation processes by up to 88%, allowing teams to react swiftly and thwart breaches effectively. The setup process is quick, enabling users to initiate operations within minutes and access a centralized repository of information regarding all cloud assets and security settings across various multi-cloud environments and accounts. With its comprehensive capabilities, Falcon Horizon not only enhances security but also streamlines operational efficiency for organizations navigating complex cloud landscapes.

Falcon Identity Threat Detection provides a comprehensive view of all Service and Privileged accounts across both your network and cloud environments, offering detailed credential profiles and identifying weak authentication measures across every domain. It allows for a thorough analysis of your organization’s domains to uncover potential vulnerabilities linked to outdated credentials or weak password practices, while also revealing all service connections and insecure authentication protocols in use. This solution continuously monitors both on-premises and cloud-based domain controllers through API integration, capturing all authentication traffic in real time. By establishing a behavioral baseline for all entities, it can identify unusual lateral movements, Golden Ticket attacks, Mimikatz traffic patterns, and other related security threats. Additionally, it aids in recognizing escalation of privilege and suspicious Service Account activities. With the capability to view live authentication traffic, Falcon Identity Threat Detection significantly accelerates the detection process, making it easier to identify and address incidents as they arise, thus enhancing overall security posture. Ultimately, this proactive monitoring ensures that organizations remain vigilant against potential identity-related threats.

This repository showcases the research preview of LongLLaMA, an advanced large language model that can manage extensive contexts of up to 256,000 tokens or potentially more. LongLLaMA is developed on the OpenLLaMA framework and has been fine-tuned utilizing the Focused Transformer (FoT) technique. The underlying code for LongLLaMA is derived from Code Llama. We are releasing a smaller 3B base variant of the LongLLaMA model, which is not instruction-tuned, under an open license (Apache 2.0), along with inference code that accommodates longer contexts available on Hugging Face. This model's weights can seamlessly replace LLaMA in existing systems designed for shorter contexts, specifically those handling up to 2048 tokens. Furthermore, we include evaluation results along with comparisons to the original OpenLLaMA models, thereby providing a comprehensive overview of LongLLaMA's capabilities in the realm of long-context processing.

Falcon Discover provides an exceptional way to swiftly detect and address harmful or noncompliant behaviors, delivering unparalleled real-time insight into the devices, users, and applications across your network. With a single, powerful dashboard, you can oversee all activities and easily investigate applications, accounts, and assets using both real-time and historical data. Instantly access contextual information for your systems through dynamic dashboards, graphs, charts, and advanced search functionalities that allow you to delve into supporting data. Utilize the lightweight CrowdStrike Falcon® agent to ensure your systems and users can operate without disruption. Gain a comprehensive understanding of all applications in your environment, with the ability to search for specific versions, hosts, and users. Additionally, manage non-compliance and control licensing expenses by tracking application usage effectively. Keeping a close eye on your asset inventory will aid in achieving, sustaining, and demonstrating compliance with regulatory requirements while enhancing overall security. By leveraging these capabilities, organizations can foster a safer and more efficient operational environment.

FalconMetrics provides a comprehensive mobile attribution and analytics solution powered by its FalconTrack™ technology, which accurately matches first-party data from ad interactions to installations while adhering to privacy regulations such as Apple’s ATT and SKAdNetwork, ensuring clarity on which advertisements lead to successful conversions without the burden of complicated dashboards. The platform includes lightweight SDKs designed for Android, iOS, and Flutter, allowing seamless integration with advertising networks and application events, which facilitates real-time insights and optimization within native ad interfaces, as well as supporting event and conversion tracking for actions like sign-ups and purchases with privacy-conscious handling of IPs and identifiers. With a focus on a Flutter-first design, FalconMetrics ensures quick implementation, optimal performance, and unified support across various platforms using a single codebase. The service prioritizes straightforward and transparent integration with leading ad networks such as Meta, aiming to capture all significant conversion data efficiently and effectively for users. Notably, it also provides resources to help developers navigate privacy regulations while maximizing their ad performance metrics.

Qwen2.5-1M, an open-source language model from the Qwen team, has been meticulously crafted to manage context lengths reaching as high as one million tokens. This version introduces two distinct model variants, namely Qwen2.5-7B-Instruct-1M and Qwen2.5-14B-Instruct-1M, representing a significant advancement as it is the first instance of Qwen models being enhanced to accommodate such large context lengths. In addition to this, the team has released an inference framework that is based on vLLM and incorporates sparse attention mechanisms, which greatly enhance the processing speed for 1M-token inputs, achieving improvements between three to seven times. A detailed technical report accompanies this release, providing in-depth insights into the design choices and the results from various ablation studies. This transparency allows users to fully understand the capabilities and underlying technology of the models.

Mistral Large 3 pushes open-source AI into frontier territory with a massive sparse MoE architecture that activates 41B parameters per token while maintaining a highly efficient 675B total parameter design. It sets a new performance standard by combining long-context reasoning, multilingual fluency across 40+ languages, and robust multimodal comprehension within a single unified model. Trained end-to-end on thousands of NVIDIA H200 GPUs, it reaches parity with top closed-source instruction models while remaining fully accessible under the Apache 2.0 license. Developers benefit from optimized deployments through partnerships with NVIDIA, Red Hat, and vLLM, enabling smooth inference on A100, H100, and Blackwell-class systems. The model ships in both base and instruct variants, with a reasoning-enhanced version on the way for even deeper analytical capabilities. Beyond general intelligence, Mistral Large 3 is engineered for enterprise customization, allowing organizations to refine the model on internal datasets or domain-specific tasks. Its efficient token generation and powerful multimodal stack make it ideal for coding, document analysis, knowledge workflows, agentic systems, and multilingual communications. With Mistral Large 3, organizations can finally deploy frontier-class intelligence with full transparency, flexibility, and control.

Mistral Small 3.1 represents a cutting-edge, multimodal, and multilingual AI model that has been released under the Apache 2.0 license. This upgraded version builds on Mistral Small 3, featuring enhanced text capabilities and superior multimodal comprehension, while also accommodating an extended context window of up to 128,000 tokens. It demonstrates superior performance compared to similar models such as Gemma 3 and GPT-4o Mini, achieving impressive inference speeds of 150 tokens per second. Tailored for adaptability, Mistral Small 3.1 shines in a variety of applications, including instruction following, conversational support, image analysis, and function execution, making it ideal for both business and consumer AI needs. The model's streamlined architecture enables it to operate efficiently on hardware such as a single RTX 4090 or a Mac equipped with 32GB of RAM, thus supporting on-device implementations. Users can download it from Hugging Face and access it through Mistral AI's developer playground, while it is also integrated into platforms like Google Cloud Vertex AI, with additional accessibility on NVIDIA NIM and more. This flexibility ensures that developers can leverage its capabilities across diverse environments and applications.

Large language models, often requiring extensive computational resources for training over long periods, have demonstrated impressive proficiency in zero- and few-shot learning tasks. Due to the high investment needed for their development, replicating these models poses a significant challenge for many researchers. Furthermore, access to the few models available via API is limited, as users cannot obtain the complete model weights, complicating academic exploration. In response to this, we introduce Open Pre-trained Transformers (OPT), a collection of decoder-only pre-trained transformers ranging from 125 million to 175 billion parameters, which we intend to share comprehensively and responsibly with interested scholars. Our findings indicate that OPT-175B exhibits performance on par with GPT-3, yet it is developed with only one-seventh of the carbon emissions required for GPT-3's training. Additionally, we will provide a detailed logbook that outlines the infrastructure hurdles we encountered throughout the project, as well as code to facilitate experimentation with all released models, ensuring that researchers have the tools they need to explore this technology further.

IBM® Granite™ comprises a suite of AI models specifically designed for business applications, built from the ground up to prioritize trust and scalability in AI implementations. Currently, the open-source Granite models can be accessed. Our goal is to make AI widely available to as many developers as possible, which is why we have released the essential Granite Code, as well as Time Series, Language, and GeoSpatial models as open-source on Hugging Face, under the permissive Apache 2.0 license, allowing extensive commercial use without restrictions. Every Granite model is developed using meticulously selected data, ensuring exceptional transparency regarding the sources of the training data. Additionally, we have made the tools that validate and maintain the quality of this data accessible to the public, meeting the rigorous standards required for enterprise-level applications. This commitment to openness and quality reflects our dedication to fostering innovation in the AI landscape.

FalconEAM, also known as Computerized Maintenance Management System (CMMS) Software, serves as a versatile, enterprise-level asset management solution that offers a comprehensive overview of an organization's asset infrastructure, including its availability, health, productivity, and the history of both periodic and preventive maintenance as well as maintenance strategies. By minimizing unexpected downtime, FalconEAM enhances asset performance and prolongs the operational lifespan of capital equipment, thereby boosting return on assets. Maintenance tasks, whether periodic, cyclic, or reactive, are efficiently scheduled and delegated to operators. The integration of Industrial IoT (Industry 4.0) sensors along with predictive analytics enables the system to issue alerts for preventive maintenance. Furthermore, it allows for the management of assets across various business units or sites while maintaining a unified view of the entire asset infrastructure within its operational framework. Additional features such as geo-tagging and regular audit updates ensure that asset availability is accurately validated at their designated locations, promoting a more organized and efficient management approach. Overall, FalconEAM stands out as a critical tool for organizations aiming to optimize their asset management practices.

Canopy Labs has unveiled Orpheus, an innovative suite of advanced speech large language models (LLMs) aimed at achieving human-like speech generation capabilities. Utilizing the Llama-3 architecture, these models have been trained on an extensive dataset comprising over 100,000 hours of English speech, allowing them to generate speech that exhibits natural intonation, emotional depth, and rhythmic flow that outperforms existing high-end closed-source alternatives. Orpheus also features zero-shot voice cloning, enabling users to mimic voices without any need for prior fine-tuning, and provides easy-to-use tags for controlling emotion and intonation. The models are engineered for low latency, achieving approximately 200ms streaming latency for real-time usage, which can be further decreased to around 100ms when utilizing input streaming. Canopy Labs has made available both pre-trained and fine-tuned models with 3 billion parameters under the flexible Apache 2.0 license, with future intentions to offer smaller models with 1 billion, 400 million, and 150 million parameters to cater to devices with limited resources. This strategic move is expected to broaden accessibility and application potential across various platforms and use cases.