Qwen
Models by this creator
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Qwen-7B-Chat
742
Qwen-7B-Chat is a large language model developed by Qwen, a team from Alibaba Cloud. It is a transformer-based model that has been pretrained on a large volume of data including web texts, books, and code. Qwen-7B-Chat is an aligned version of the Qwen-7B model, trained using techniques to improve the model's conversational abilities. Compared to similar models like Baichuan-7B, Qwen-7B-Chat leverages the Qwen model series which has been optimized for both Chinese and English. The model achieves strong performance on standard benchmarks like C-EVAL and MMLU. Unlike LLaMA, which prohibits commercial use, Qwen-7B-Chat has a more permissive open-source license that allows for commercial applications. Model Inputs and Outputs Inputs Text prompts**: Qwen-7B-Chat accepts text prompts as input, which can be used to initiate conversations or provide instructions for the model. Outputs Text responses**: The model generates coherent and contextually relevant text responses based on the input prompts. The responses aim to be informative, engaging, and helpful for the user. Capabilities Qwen-7B-Chat demonstrates strong performance across a variety of natural language tasks, including open-ended conversations, question answering, summarization, and even code generation. The model can engage in multi-turn dialogues, maintain context, and provide detailed and thoughtful responses. For example, when prompted with "Tell me about the history of the internet", Qwen-7B-Chat is able to provide a comprehensive overview covering the key developments and milestones in the history of the internet, drawing upon its broad knowledge base. What Can I Use It For? Qwen-7B-Chat can be a valuable tool for a wide range of applications, including: Conversational AI assistants**: The model's strong conversational abilities make it well-suited for building engaging and intelligent virtual assistants that can help with a variety of tasks. Content generation**: Qwen-7B-Chat can be used to generate high-quality text content, such as articles, stories, or even marketing copy, by providing relevant prompts. Chatbots and customer service**: The model's ability to understand and respond to natural language queries makes it a good fit for building chatbots and virtual customer service agents. Educational applications**: Qwen-7B-Chat can be used to create interactive learning experiences, answer questions, and provide explanations on a variety of topics. Things to Try One interesting aspect of Qwen-7B-Chat is its ability to engage in open-ended conversations and provide detailed, contextually relevant responses. For example, try prompting the model with a more abstract or philosophical question, such as "What is the meaning of life?" or "How can we achieve true happiness?" The model's responses can provide interesting insights and perspectives, showcasing its depth of understanding and reasoning capabilities. Another area to explore is the model's ability to handle complex tasks, such as providing step-by-step instructions for a multi-part process or generating coherent and logical code snippets. By testing the model's capabilities in these more challenging areas, you can gain a better understanding of its strengths and limitations.
Updated 5/28/2024
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Qwen2-72B-Instruct
453
Qwen2-72B-Instruct is the 72 billion parameter version of the Qwen2 series of large language models developed by Qwen. Compared to the state-of-the-art open-source language models, including the previous Qwen1.5 release, Qwen2 has generally surpassed most open-source models and demonstrated competitiveness against proprietary models across a range of benchmarks targeting language understanding, generation, multilingual capability, coding, mathematics, and reasoning. The Qwen2-72B-Instruct model specifically has been instruction-tuned, enabling it to excel at a variety of tasks. The Qwen2 series, including the Qwen2-7B-Instruct and Qwen2-72B models, is based on the Transformer architecture with improvements like SwiGLU activation, attention QKV bias, and group query attention. Qwen has also developed an improved tokenizer that is adaptive to multiple natural languages and codes. Model inputs and outputs Inputs Text prompts for language generation, translation, summarization, and other language tasks Outputs Texts generated in response to the input prompts, with the model demonstrating strong performance on a variety of natural language processing tasks. Capabilities The Qwen2-72B-Instruct model has shown strong performance on a range of benchmarks, including language understanding, generation, multilingual capability, coding, mathematics, and reasoning. For example, it surpassed open-source models like LLaMA and Yi on the MMLU (Multimodal Language Understanding) benchmark, and outperformed them on coding tasks like HumanEval and MultiPL-E. The model also exhibited competitive performance against proprietary models like ChatGPT on Chinese language benchmarks like C-Eval. What can I use it for? The Qwen2-72B-Instruct model can be used for a variety of natural language processing tasks, including text generation, language translation, summarization, and question answering. Its strong performance on coding and mathematical reasoning benchmarks also makes it suitable for applications like code generation and problem-solving. Given its multilingual capabilities, the model can be leveraged for international and cross-cultural projects. Things to try One interesting aspect of the Qwen2-72B-Instruct model is its ability to handle long input texts. By utilizing the YARN technique for enhancing model length extrapolation, the model can process inputs up to 131,072 tokens, enabling the processing of extensive texts. This could be useful for applications that require working with large amounts of textual data, such as document summarization or question answering over lengthy passages.
Updated 7/1/2024
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Qwen-14B-Chat
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Qwen-14B-Chat is the 14B-parameter version of the large language model series, Qwen (abbr. Tongyi Qianwen), proposed by Alibaba Cloud. Qwen-14B-Chat is a Transformer-based large language model that has been pretrained on a large volume of data, including web texts, books, and code. It has been further trained using alignment techniques to create an AI assistant with strong language understanding and generation capabilities. Compared to the Qwen-7B-Chat model, Qwen-14B-Chat has double the parameter count and can thus handle more complex tasks and generate more coherent and relevant responses. It outperforms other similarly-sized models on a variety of benchmarks such as C-Eval, MMLU, and GSM8K. Model inputs and outputs Inputs Free-form text prompts, which can include instructions, questions, or open-ended statements. The model supports multi-turn dialogues, where the input can include the conversation history. Outputs Coherent, contextually relevant text responses generated by the model. The model can generate responses of varying length, from short single-sentence replies to longer multi-paragraph outputs. Capabilities Qwen-14B-Chat has demonstrated strong performance on a wide range of tasks, including language understanding, reasoning, code generation, and tool usage. It achieves state-of-the-art results on benchmarks like C-Eval and MMLU, outperforming other large language models of similar size. The model also supports ReAct prompting, allowing it to call external APIs and plugins to assist with tasks that require accessing external information or functionality. This enables the model to handle more complex and open-ended prompts that require accessing external tools or data. What can I use it for? Given its impressive capabilities, Qwen-14B-Chat can be a valuable tool for a variety of applications. Some potential use cases include: Content generation**: The model can be used to generate high-quality text content such as articles, stories, or creative writing. Its strong language understanding and generation abilities make it well-suited for tasks like writing assistance, ideation, and summarization. Conversational AI**: Qwen-14B-Chat's ability to engage in coherent, multi-turn dialogues makes it a promising candidate for building advanced chatbots and virtual assistants. Its ReAct prompting support also allows it to be integrated with other tools and services. Task automation**: By leveraging the model's capabilities in areas like code generation, mathematical reasoning, and tool usage, it can be used to automate a variety of tasks that require language-based intelligence. Research and experimentation**: As an open-source model, Qwen-14B-Chat provides a powerful platform for researchers and developers to explore the capabilities of large language models and experiment with new techniques and applications. Things to try One interesting aspect of Qwen-14B-Chat is its strong performance on long-context tasks, thanks to the inclusion of techniques like NTK-aware interpolation and LogN attention scaling. Researchers and developers can experiment with using the model for tasks that require understanding and generating text with extended context, such as document summarization, long-form question answering, or multi-turn task-oriented dialogues. Another intriguing area to explore is the model's ReAct prompting capabilities, which allow it to interact with external APIs and plugins. Users can try integrating Qwen-14B-Chat with a variety of tools and services to see how it can be leveraged for more complex, real-world applications that go beyond simple language generation.
Updated 5/28/2024
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Qwen-7B
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Qwen-7B is the 7B-parameter version of the large language model series, Qwen (abbr. Tongyi Qianwen), proposed by Alibaba Cloud. Qwen-7B is a Transformer-based large language model, which is pretrained on a large volume of data, including web texts, books, codes, etc. Additionally, based on the pretrained Qwen-7B, the maintainers release Qwen-7B-Chat, a large-model-based AI assistant, which is trained with alignment techniques. Qwen-7B significantly surpasses existing open-source models of similar scale on multiple Chinese and English downstream evaluation tasks, and even outperforms some larger-scale models in several benchmarks. Compared to other open-source models, Qwen-7B uses a more comprehensive vocabulary of over 150K tokens, which is more friendly to multiple languages. Model inputs and outputs Inputs Text prompt**: Qwen-7B accepts text prompts as input to generate output text. Outputs Generated text**: Qwen-7B generates relevant text based on the input prompt. Capabilities Qwen-7B demonstrates strong performance on a variety of benchmarks, including commonsense reasoning, coding, mathematics, and more. The model is also capable of engaging in open-ended conversation through the Qwen-7B-Chat version. What can I use it for? Qwen-7B and Qwen-7B-Chat can be used for a wide range of natural language processing tasks, such as text generation, question answering, and language understanding. The large-scale pretraining and strong performance make these models suitable for tasks like content creation, customer service chatbots, and even code generation. The maintainers also provide an API for users to integrate the models into their applications. Things to try Given Qwen-7B's strong performance on benchmarks, users can experiment with fine-tuning the model on specialized datasets to further enhance its capabilities for specific domains or tasks. The maintainers also provide intermediate checkpoints during the pretraining process, which can be used to study the model's learning dynamics. Additionally, the quantized versions of Qwen-7B-Chat offer improved inference speed and memory usage, making them suitable for deployment on resource-constrained environments.
Updated 5/28/2024
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Qwen2-7B-Instruct
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The Qwen2-7B-Instruct is the 7 billion parameter instruction-tuned language model from the Qwen2 series of large language models developed by Qwen. Compared to state-of-the-art open-source language models like LLaMA and ChatGLM, the Qwen2 series has generally surpassed them in performance across a range of benchmarks targeting language understanding, generation, multilingual capabilities, coding, mathematics, and reasoning. The Qwen2 series includes models ranging from 0.5 to 72 billion parameters, with the Qwen2-7B-Instruct being one of the smaller yet capable instruction-tuned variants. It is based on the Transformer architecture with enhancements like SwiGLU activation, attention QKV bias, and group query attention. The model also uses an improved tokenizer that is adaptive to multiple natural languages and coding. Model inputs and outputs Inputs Text**: The model can take text inputs of up to 131,072 tokens, enabling processing of extensive inputs. Outputs Text**: The model generates text outputs, which can be used for a variety of natural language tasks such as question answering, summarization, and creative writing. Capabilities The Qwen2-7B-Instruct model has shown strong performance across a range of benchmarks, including language understanding (MMLU, C-Eval), mathematics (GSM8K, MATH), coding (HumanEval, MBPP), and reasoning (BBH). It has demonstrated competitiveness against proprietary models in these areas. What can I use it for? The Qwen2-7B-Instruct model can be used for a variety of natural language processing tasks, such as: Question answering**: The model can be used to answer questions on a wide range of topics, drawing upon its broad knowledge base. Summarization**: The model can be used to generate concise summaries of long-form text, such as articles or reports. Creative writing**: The model can be used to generate original text, such as stories, poems, or scripts, with its strong language generation capabilities. Coding assistance**: The model's coding knowledge can be leveraged to help with tasks like code generation, explanation, and debugging. Things to try One interesting aspect of the Qwen2-7B-Instruct model is its ability to process long-form text inputs, thanks to its large context length of up to 131,072 tokens. This can be particularly useful for tasks that require understanding and reasoning over extensive information, such as academic papers, legal documents, or historical archives. Another area to explore is the model's multilingual capabilities. As mentioned, the Qwen2 series, including the Qwen2-7B-Instruct, has been designed to be adaptive to multiple languages, which could make it a valuable tool for cross-lingual applications.
Updated 7/1/2024
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Qwen-72B
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Qwen-72B is the 72B-parameter version of the large language model series, Qwen (abbr. Tongyi Qianwen), proposed by Alibaba Cloud. Qwen-72B is a Transformer-based large language model, which is pretrained on a large volume of data, including web texts, books, codes, etc. Additionally, based on the pretrained Qwen-72B, Qwen releases Qwen-72B-Chat, a large-model-based AI assistant, which is trained with alignment techniques. Key features of Qwen-72B include: Large-scale high-quality training corpora: It is pretrained on over 3 trillion tokens, including Chinese, English, multilingual texts, code, and mathematics, covering general and professional fields. Competitive performance: It significantly surpasses existing open-source models on multiple Chinese and English downstream evaluation tasks. More comprehensive vocabulary coverage: Compared to other models, Qwen-72B uses a vocabulary of over 150K tokens, allowing for more efficient encoding of multiple languages. Longer context support: Qwen-72B supports a context length of up to 32k tokens. Model inputs and outputs Inputs Text**: Qwen-72B accepts text inputs in a variety of languages, including Chinese, English, and others. Outputs Text**: Qwen-72B generates fluent and coherent text outputs in response to the input, drawing upon its broad knowledge base. Code**: In addition to natural language, Qwen-72B can also generate code in various programming languages. Capabilities Qwen-72B demonstrates impressive performance on a wide range of tasks, including commonsense reasoning, language understanding, mathematical problem-solving, and code generation. For example, it achieves state-of-the-art results on benchmarks like MMLU, C-Eval, and HumanEval, outperforming many other large language models of similar or even larger scale. What can I use it for? With its broad capabilities, Qwen-72B can be leveraged for a variety of applications, such as: Content creation**: Generating high-quality text, articles, stories, and dialogues in multiple languages. Conversational AI**: Powering intelligent chatbots and virtual assistants with advanced language understanding and generation abilities. Code generation and programming**: Assisting developers with tasks like code completion, refactoring, and even full-fledged program generation. Multilingual applications**: Developing multilingual applications that can seamlessly handle and translate between various languages. Things to try One interesting aspect of Qwen-72B is its ability to handle long-form text and extended context. You could try generating coherent and relevant output based on lengthy prompts or multi-turn dialogues, exploring how the model maintains context and produces consistent responses over time. Another area to experiment with is the model's code generation capabilities. You could provide Qwen-72B with programming prompts or partially completed code snippets and observe how it can extend and refine the code to solve specific tasks or implement desired functionalities.
Updated 5/28/2024
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Qwen-VL-Chat
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Qwen-VL-Chat is a large vision language model proposed by Alibaba Cloud. It is the visual multimodal version of the Qwen (Tongyi Qianwen) large model series. Qwen-VL-Chat accepts image, text, and bounding box as inputs, and outputs text and bounding box. It is a more capable version of the base Qwen-VL model. Qwen-VL-Chat is pretrained on large-scale data and can be used for a variety of vision-language tasks such as image captioning, visual question answering, and referring expression comprehension. Compared to the base Qwen-VL model, Qwen-VL-Chat has enhanced capabilities for interactive visual dialogue. Model inputs and outputs Inputs Image**: An image in the form of a tensor Text**: A textual prompt or dialogue history Bounding box**: Locations of objects or regions of interest in the image Outputs Text**: The model's generated response text Bounding box**: Locations of objects or regions referred to in the output text Capabilities Qwen-VL-Chat can perform a wide range of vision-language tasks, including: Image captioning: Generating descriptions for images Visual question answering: Answering questions about the content of images Referring expression comprehension: Localizing objects or regions in images based on textual referring expressions Visual dialogue: Engaging in back-and-forth conversations about images, by understanding the visual context and generating relevant responses The model leverages both visual and textual information to produce more accurate and contextually appropriate outputs compared to models that only use text or vision alone. What can I use it for? Qwen-VL-Chat can be used in a variety of applications that involve understanding and reasoning about visual information, such as: Intelligent image search and retrieval: Allowing users to search for and retrieve relevant images using natural language queries. Automated image captioning and description generation: Generating descriptive captions for images to aid accessibility or summarize visual content. Visual question answering: Building AI assistants that can answer questions about the contents of images. Interactive visual dialogue systems: Creating chatbots that can engage in back-and-forth conversations about images, answering follow-up questions and providing additional information. Multimodal content creation and editing: Assisting users in creating and manipulating visual content by understanding both the image and textual context. These capabilities can be leveraged in a wide range of industries, such as e-commerce, education, entertainment, and more. Things to try One interesting aspect of Qwen-VL-Chat is its ability to ground language in visual context and generate responses that are tailored to the specific image being discussed. For example, you could try providing the model with an image and a question about the contents of the image, and see how it leverages the visual information to provide a detailed and relevant answer. Another interesting area to explore is the model's capacity for interactive visual dialogue. You could try engaging the model in a back-and-forth conversation about an image, asking follow-up questions or providing additional context, and observe how it updates its understanding and generates appropriate responses. Additionally, you could experiment with using Qwen-VL-Chat for tasks like image captioning or referring expression comprehension, and compare its performance to other vision-language models. This could help you better understand the model's strengths and limitations in different applications.
Updated 5/28/2024
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Qwen1.5-72B-Chat
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Qwen1.5-72B-Chat is the beta version of the Qwen2 large language model, a transformer-based decoder-only model pretrained on a vast amount of data. Compared to the previous Qwen model, improvements include larger model sizes up to 72B parameters, significant performance gains in human preference for chat models, multilingual support, and stable support for 32K context length. The Qwen1.5-72B model is another large 72B parameter version from the Qwen series, focused on general language modeling performance. In contrast, the Qwen1.5-72B-Chat model is specifically optimized for chatbot-style dialog. Model Inputs and Outputs Inputs Text prompts**: The model accepts natural language text prompts as input, which can be questions, statements, or open-ended requests. Chat history**: The model can also take in previous dialog context to continue a multi-turn conversation. Outputs Generated text**: The primary output of the model is continuations of the input text, generating coherent and contextually relevant responses. Multilingual support**: The model is capable of understanding and generating text in multiple languages, including Chinese, English, and others. Capabilities The Qwen1.5-72B-Chat model exhibits strong performance across a variety of benchmarks, outperforming similarly-sized open-source models. It demonstrates robust capabilities in language understanding, reasoning, and generation, as evidenced by its high scores on evaluations like MMLU, C-Eval, and GSM8K. The model also shows impressive abilities in tasks like code generation, with a HumanEval zero-shot pass@1 score of 37.2%. Additionally, it exhibits strong long-context understanding, achieving a VCSUM Rouge-L score of 16.6 on a long-form summarization dataset. What Can I Use It For? The Qwen1.5-72B-Chat model can be a powerful tool for building advanced conversational AI applications. Its multilingual capabilities and strong performance on dialog-oriented benchmarks make it well-suited for developing intelligent chatbots, virtual assistants, and other language-based interfaces. Potential use cases include customer service automation, personal productivity assistants, educational tutors, and creative writing aides. The model's broad knowledge and reasoning skills also enable it to assist with research, analysis, and problem-solving tasks across various domains. Things to Try One interesting aspect of the Qwen1.5-72B-Chat model is its ability to utilize external tools and APIs through "ReAct Prompting". This allows the model to dynamically call upon relevant plugins or APIs to enhance its capabilities, such as performing web searches, accessing databases, or invoking specialized computational engines. Developers could experiment with integrating the model into a broader system architecture that leverages these external capabilities, enabling the chatbot to provide more comprehensive and actionable responses to user queries. The model's strong performance on the HuggingFace Agent benchmark suggests it is well-suited for this type of hybrid AI approach.
Updated 5/28/2024
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Qwen-14B
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Qwen-14B is the 14B-parameter version of the large language model series, Qwen (abbr. Tongyi Qianwen), proposed by Alibaba Cloud. Qwen-14B is a Transformer-based large language model, which is pretrained on a large volume of data, including web texts, books, codes, etc. Additionally, based on the pretrained Qwen-14B, Qwen-14B-Chat is released, a large-model-based AI assistant, which is trained with alignment techniques. Qwen-14B features a large-scale high-quality training corpus of over 3 trillion tokens, covering Chinese, English, multilingual texts, code, and mathematics. It significantly surpasses existing open-source models of similar scale on multiple Chinese and English downstream evaluation tasks. Qwen-14B also uses a more comprehensive vocabulary of over 150K tokens, enabling users to directly enhance capabilities for certain languages without expanding the vocabulary. Model inputs and outputs Inputs Text**: Qwen-14B accepts text input of up to 2048 tokens. Outputs Text**: Qwen-14B generates text output in response to the input. Capabilities Qwen-14B demonstrates competitive performance across a range of benchmarks. On the C-Eval Chinese evaluation, it achieves 69.8% zero-shot and 71.7% 5-shot accuracy, outperforming similarly-sized models. On MMLU, its zero-shot and 5-shot English evaluation accuracy reaches 64.6% and 66.5% respectively. Qwen-14B also performs well on coding tasks, scoring 43.9% on the HumanEval zero-shot benchmark, and 60.1% on the zero-shot GSM8K mathematics evaluation. What can I use it for? The large scale and broad capabilities of Qwen-14B make it suitable for a variety of natural language processing tasks. Potential use cases include: Content generation**: Qwen-14B can be used to generate high-quality text on a wide range of topics, from creative writing to technical documentation. Conversational AI**: Building on the Qwen-14B-Chat model, developers can create advanced chatbots and virtual assistants. Multilingual support**: The model's comprehensive vocabulary allows it to handle multiple languages, enabling cross-lingual applications. Code generation and reasoning**: Qwen-14B's strong performance on coding and math tasks makes it useful for programming-related applications. Things to try One interesting aspect of Qwen-14B is its ability to handle long-form text. By incorporating techniques like NTK-aware interpolation and LogN attention scaling, the model can maintain strong performance even on sequences up to 32,768 tokens long. Developers could explore leveraging this capability for tasks like long-form summarization or knowledge-intensive QA. Another intriguing area to experiment with is Qwen-14B's tool usage capabilities. The model supports ReAct prompting, allowing it to interact with external plugins and APIs. This could enable the development of intelligent assistants that can seamlessly integrate diverse functionalities.
Updated 5/28/2024
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CodeQwen1.5-7B-Chat
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CodeQwen1.5-7B-Chat is a transformer-based language model developed by Qwen. It is a code-specific version of the larger Qwen1.5 model series, which includes language models of various sizes. CodeQwen1.5-7B-Chat is trained on a large amount of code data and excels at tasks like text-to-SQL, bug fixing, and more. Compared to the original Qwen1.5 model, CodeQwen1.5-7B-Chat has strong code generation capabilities and can handle long contexts of up to 64K tokens across 92 coding languages. Model inputs and outputs Inputs Text**: CodeQwen1.5-7B-Chat can accept text inputs for various code-related tasks, such as prompts for code generation, text-to-SQL, and bug fixes. Outputs Text**: The model generates text outputs, which can include code, SQL queries, or natural language responses related to the input. Capabilities CodeQwen1.5-7B-Chat demonstrates impressive performance across a range of benchmarks, including text-to-SQL, bug fixing, and more. It can generate high-quality code and maintain coherence over long contexts of up to 64K tokens. What can I use it for? CodeQwen1.5-7B-Chat can be a valuable tool for developers and data analysts who need assistance with code-related tasks. It can be used to generate code snippets, fix bugs, translate natural language to SQL queries, and more. The model's strong performance and ability to handle long contexts make it well-suited for complex, multi-step coding and data analysis projects. Things to try One interesting aspect of CodeQwen1.5-7B-Chat is its support for a wide range of coding languages, which allows users to directly enhance the model's capabilities in specific languages without the need to expand the vocabulary. This can be particularly useful for developers working in less common programming languages or those who need multilingual support for their projects.
Updated 5/28/2024