LLaMA-Factory Integrations

OpenAI aims to guarantee that artificial general intelligence (AGI)—defined as highly autonomous systems excelling beyond human capabilities in most economically significant tasks—serves the interests of all humanity. While we intend to develop safe and advantageous AGI directly, we consider our mission successful if our efforts support others in achieving this goal. You can utilize our API for a variety of language-related tasks, including semantic search, summarization, sentiment analysis, content creation, translation, and beyond, all with just a few examples or by clearly stating your task in English. A straightforward integration provides you with access to our continuously advancing AI technology, allowing you to explore the API’s capabilities through these illustrative completions and discover numerous potential applications.

DeepSeek stands out as a state-of-the-art AI assistant, leveraging the sophisticated DeepSeek-V3 model that boasts an impressive 600 billion parameters for superior performance. Created to rival leading AI systems globally, it delivers rapid responses alongside an extensive array of features aimed at enhancing daily tasks' efficiency and simplicity. Accessible on various platforms, including iOS, Android, and web, DeepSeek guarantees that users can connect from virtually anywhere. The application offers support for numerous languages and is consistently updated to enhance its capabilities, introduce new language options, and fix any issues. Praised for its smooth functionality and adaptability, DeepSeek has received enthusiastic reviews from a diverse user base around the globe. Furthermore, its commitment to user satisfaction and continuous improvement ensures that it remains at the forefront of AI technology.

Mistral AI stands out as an innovative startup in the realm of artificial intelligence, focusing on open-source generative solutions. The company provides a diverse array of customizable, enterprise-level AI offerings that can be implemented on various platforms, such as on-premises, cloud, edge, and devices. Among its key products are "Le Chat," a multilingual AI assistant aimed at boosting productivity in both personal and professional settings, and "La Plateforme," a platform for developers that facilitates the creation and deployment of AI-driven applications. With a strong commitment to transparency and cutting-edge innovation, Mistral AI has established itself as a prominent independent AI laboratory, actively contributing to the advancement of open-source AI and influencing policy discussions. Their dedication to fostering an open AI ecosystem underscores their role as a thought leader in the industry.

Qwen LLM represents a collection of advanced large language models created by Alibaba Cloud's Damo Academy. These models leverage an extensive dataset comprising text and code, enabling them to produce human-like text, facilitate language translation, craft various forms of creative content, and provide informative answers to queries. Key attributes of Qwen LLMs include: A range of sizes: The Qwen series features models with parameters varying from 1.8 billion to 72 billion, catering to diverse performance requirements and applications. Open source availability: Certain versions of Qwen are open-source, allowing users to access and modify the underlying code as needed. Multilingual capabilities: Qwen is equipped to comprehend and translate several languages, including English, Chinese, and French. Versatile functionalities: In addition to language generation and translation, Qwen models excel in tasks such as answering questions, summarizing texts, and generating code, making them highly adaptable tools for various applications. Overall, the Qwen LLM family stands out for its extensive capabilities and flexibility in meeting user needs.

The Mixtral 8x22B represents our newest open model, establishing a new benchmark for both performance and efficiency in the AI sector. This sparse Mixture-of-Experts (SMoE) model activates only 39B parameters from a total of 141B, ensuring exceptional cost efficiency relative to its scale. Additionally, it demonstrates fluency in multiple languages, including English, French, Italian, German, and Spanish, while also possessing robust skills in mathematics and coding. With its native function calling capability, combined with the constrained output mode utilized on la Plateforme, it facilitates the development of applications and the modernization of technology stacks on a large scale. The model's context window can handle up to 64K tokens, enabling accurate information retrieval from extensive documents. We prioritize creating models that maximize cost efficiency for their sizes, thereby offering superior performance-to-cost ratios compared to others in the community. The Mixtral 8x22B serves as a seamless extension of our open model lineage, and its sparse activation patterns contribute to its speed, making it quicker than any comparable dense 70B model on the market. Furthermore, its innovative design positions it as a leading choice for developers seeking high-performance solutions.

ChatGLM-6B is a bilingual dialogue model that supports both Chinese and English, built on the General Language Model (GLM) framework and features 6.2 billion parameters. Thanks to model quantization techniques, it can be easily run on standard consumer graphics cards, requiring only 6GB of video memory at the INT4 quantization level. This model employs methodologies akin to those found in ChatGPT but is specifically tailored to enhance Chinese question-and-answer interactions and dialogue. Following extensive training with approximately 1 trillion identifiers in both languages, along with additional supervision, fine-tuning, self-assistance through feedback, and reinforcement learning from human input, ChatGLM-6B has demonstrated an impressive capability to produce responses that resonate well with human users. Its adaptability and performance make it a valuable tool for bilingual communication.

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.

We have incorporated Llama 3 into Meta AI, our intelligent assistant that enhances how individuals accomplish tasks, innovate, and engage with Meta AI. By utilizing Meta AI for coding and problem-solving, you can experience Llama 3's capabilities first-hand. Whether you are creating agents or other AI-driven applications, Llama 3, available in both 8B and 70B versions, will provide the necessary capabilities and flexibility to bring your ideas to fruition. With the launch of Llama 3, we have also revised our Responsible Use Guide (RUG) to offer extensive guidance on the ethical development of LLMs. Our system-focused strategy encompasses enhancements to our trust and safety mechanisms, including Llama Guard 2, which is designed to align with the newly introduced taxonomy from MLCommons, broadening its scope to cover a wider array of safety categories, alongside code shield and Cybersec Eval 2. Additionally, these advancements aim to ensure a safer and more responsible use of AI technologies in various applications.

TensorBoard serves as a robust visualization platform within TensorFlow, specifically crafted to aid in the experimentation process of machine learning. It allows users to monitor and illustrate various metrics, such as loss and accuracy, while also offering insights into the model architecture through visual representations of its operations and layers. Users can observe the evolution of weights, biases, and other tensors via histograms over time, and it also allows for the projection of embeddings into a more manageable lower-dimensional space, along with the capability to display various forms of data, including images, text, and audio. Beyond these visualization features, TensorBoard includes profiling tools that help streamline and enhance the performance of TensorFlow applications. Collectively, these functionalities equip practitioners with essential tools for understanding, troubleshooting, and refining their TensorFlow projects, ultimately improving the efficiency of the machine learning process. In the realm of machine learning, accurate measurement is crucial for enhancement, and TensorBoard fulfills this need by supplying the necessary metrics and visual insights throughout the workflow. This platform not only tracks various experimental metrics but also facilitates the visualization of complex model structures and the dimensionality reduction of embeddings, reinforcing its importance in the machine learning toolkit.

LLaVA, or Large Language-and-Vision Assistant, represents a groundbreaking multimodal model that combines a vision encoder with the Vicuna language model, enabling enhanced understanding of both visual and textual information. By employing end-to-end training, LLaVA showcases remarkable conversational abilities, mirroring the multimodal features found in models such as GPT-4. Significantly, LLaVA-1.5 has reached cutting-edge performance on 11 different benchmarks, leveraging publicly accessible data and achieving completion of its training in about one day on a single 8-A100 node, outperforming approaches that depend on massive datasets. The model's development included the construction of a multimodal instruction-following dataset, which was produced using a language-only variant of GPT-4. This dataset consists of 158,000 distinct language-image instruction-following examples, featuring dialogues, intricate descriptions, and advanced reasoning challenges. Such a comprehensive dataset has played a crucial role in equipping LLaVA to handle a diverse range of tasks related to vision and language with great efficiency. In essence, LLaVA not only enhances the interaction between visual and textual modalities but also sets a new benchmark in the field of multimodal AI.

Yi-Large is an innovative proprietary large language model created by 01.AI, featuring an impressive context length of 32k and a cost structure of $2 for each million tokens for both inputs and outputs. Renowned for its superior natural language processing abilities, common-sense reasoning, and support for multiple languages, it competes effectively with top models such as GPT-4 and Claude3 across various evaluations. This model is particularly adept at handling tasks that involve intricate inference, accurate prediction, and comprehensive language comprehension, making it ideal for applications such as knowledge retrieval, data categorization, and the development of conversational chatbots that mimic human interaction. Built on a decoder-only transformer architecture, Yi-Large incorporates advanced features like pre-normalization and Group Query Attention, and it has been trained on an extensive, high-quality multilingual dataset to enhance its performance. The model's flexibility and economical pricing position it as a formidable player in the artificial intelligence landscape, especially for businesses looking to implement AI technologies on a global scale. Additionally, its ability to adapt to a wide range of use cases underscores its potential to revolutionize how organizations leverage language models for various needs.

MLflow is an open-source suite designed to oversee the machine learning lifecycle, encompassing aspects such as experimentation, reproducibility, deployment, and a centralized model registry. The platform features four main components that facilitate various tasks: tracking and querying experiments encompassing code, data, configurations, and outcomes; packaging data science code to ensure reproducibility across multiple platforms; deploying machine learning models across various serving environments; and storing, annotating, discovering, and managing models in a unified repository. Among these, the MLflow Tracking component provides both an API and a user interface for logging essential aspects like parameters, code versions, metrics, and output files generated during the execution of machine learning tasks, enabling later visualization of results. It allows for logging and querying experiments through several interfaces, including Python, REST, R API, and Java API. Furthermore, an MLflow Project is a structured format for organizing data science code, ensuring it can be reused and reproduced easily, with a focus on established conventions. Additionally, the Projects component comes equipped with an API and command-line tools specifically designed for executing these projects effectively. Overall, MLflow streamlines the management of machine learning workflows, making it easier for teams to collaborate and iterate on their models.

Introducing Gemma, your innovative AI companion designed to spark creativity and streamline your workflow. With Gemma, you can brainstorm fresh ideas, enhance current designs, and handle repetitive tasks, allowing you to concentrate on what truly inspires you. Whether you need assistance crafting compelling headlines, engaging body text, or memorable brand names, Gemma is here to help. Additionally, Gemma can generate highly realistic images that can be easily resized and modified to suit your needs. Available around the clock, Gemma’s user-friendly interface opens the door to a multitude of AI models and integrates seamlessly with the creative tools you already use. With a focus on learning from your input and preferences, Gemma offers unique suggestions and valuable insights that can elevate your projects. Installing Gemma on your desktop is a breeze, enabling you to access this powerful tool across various files and applications effortlessly. Say goodbye to the intimidating blank page, as Gemma’s cutting-edge algorithms empower your artistic pursuits and transform your visions into reality. You’ll find that collaborating with Gemma is like having a creative partner by your side, ready to explore new horizons together.

We are excited to announce the launch of Phi-2, a language model featuring 2.7 billion parameters that excels in reasoning and language comprehension, achieving top-tier results compared to other base models with fewer than 13 billion parameters. In challenging benchmarks, Phi-2 competes with and often surpasses models that are up to 25 times its size, a feat made possible by advancements in model scaling and meticulous curation of training data. Due to its efficient design, Phi-2 serves as an excellent resource for researchers interested in areas such as mechanistic interpretability, enhancing safety measures, or conducting fine-tuning experiments across a broad spectrum of tasks. To promote further exploration and innovation in language modeling, Phi-2 has been integrated into the Azure AI Studio model catalog, encouraging collaboration and development within the research community. Researchers can leverage this model to unlock new insights and push the boundaries of language technology.

PaliGemma 2 represents the next step forward in tunable vision-language models, enhancing the already capable Gemma 2 models by integrating visual capabilities and simplifying the process of achieving outstanding performance through fine-tuning. This advanced model enables users to see, interpret, and engage with visual data, thereby unlocking an array of innovative applications. It comes in various sizes (3B, 10B, 28B parameters) and resolutions (224px, 448px, 896px), allowing for adaptable performance across different use cases. PaliGemma 2 excels at producing rich and contextually appropriate captions for images, surpassing basic object recognition by articulating actions, emotions, and the broader narrative associated with the imagery. Our research showcases its superior capabilities in recognizing chemical formulas, interpreting music scores, performing spatial reasoning, and generating reports for chest X-rays, as elaborated in the accompanying technical documentation. Transitioning to PaliGemma 2 is straightforward for current users, ensuring a seamless upgrade experience while expanding their operational potential. The model's versatility and depth make it an invaluable tool for both researchers and practitioners in various fields.

TensorWave is a cloud platform designed for AI and high-performance computing (HPC), exclusively utilizing AMD Instinct Series GPUs to ensure optimal performance. It features a high-bandwidth and memory-optimized infrastructure that seamlessly scales to accommodate even the most rigorous training or inference tasks. Users can access AMD’s leading GPUs in mere seconds, including advanced models like the MI300X and MI325X, renowned for their exceptional memory capacity and bandwidth, boasting up to 256GB of HBM3E and supporting speeds of 6.0TB/s. Additionally, TensorWave's architecture is equipped with UEC-ready functionalities that enhance the next generation of Ethernet for AI and HPC networking, as well as direct liquid cooling systems that significantly reduce total cost of ownership, achieving energy cost savings of up to 51% in data centers. The platform also incorporates high-speed network storage, which provides transformative performance, security, and scalability for AI workflows. Furthermore, it ensures seamless integration with a variety of tools and platforms, accommodating various models and libraries to enhance user experience. TensorWave stands out for its commitment to performance and efficiency in the evolving landscape of AI technology.

Llama (Large Language Model Meta AI) stands as a cutting-edge foundational large language model aimed at helping researchers push the boundaries of their work within this area of artificial intelligence. By providing smaller yet highly effective models like Llama, the research community can benefit even if they lack extensive infrastructure, thus promoting greater accessibility in this dynamic and rapidly evolving domain. Creating smaller foundational models such as Llama is advantageous in the landscape of large language models, as it demands significantly reduced computational power and resources, facilitating the testing of innovative methods, confirming existing research, and investigating new applications. These foundational models leverage extensive unlabeled datasets, making them exceptionally suitable for fine-tuning across a range of tasks. We are offering Llama in multiple sizes (7B, 13B, 33B, and 65B parameters), accompanied by a detailed Llama model card that outlines our development process while adhering to our commitment to Responsible AI principles. By making these resources available, we aim to empower a broader segment of the research community to engage with and contribute to advancements in AI.