Internlm
Models by this creator
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internlm-chat-20b
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internlm-chat-20b is a large language model developed by the Shanghai Artificial Intelligence Laboratory, in collaboration with SenseTime Technology, the Chinese University of Hong Kong, and Fudan University. The model has 20 billion parameters and was pre-trained on over 2.3 trillion tokens of high-quality English, Chinese, and code data. Compared to smaller 7B and 13B models, internlm-chat-20b has a deeper architecture with 60 layers, which can enhance the model's overall capability when parameters are limited. The model has undergone SFT and RLHF training, enabling it to better and more securely meet users' needs. It exhibits significant improvements in understanding, reasoning, mathematical, and programming abilities compared to smaller models like Llama-13B, Llama2-13B, and Baichuan2-13B. Model inputs and outputs Inputs Text prompts in natural language Outputs Generated text responses to the input prompts Capabilities internlm-chat-20b has demonstrated excellent overall performance, strong utility invocation capability, and supports a 16k context length through inference extrapolation. It also exhibits better value alignment compared to other large language models. On the 5 capability dimensions proposed by OpenCompass, internlm-chat-20b has achieved the best performance within the 13B-33B parameter range, outperforming models like Llama-13B, Llama2-13B, and Baichuan2-13B. What can I use it for? internlm-chat-20b can be used for a variety of natural language processing tasks, including text generation, question answering, language translation, and code generation. The model's strong performance on understanding, reasoning, and programming tasks makes it a powerful tool for developers and researchers working on advanced AI applications. Things to try One interesting aspect of internlm-chat-20b is its ability to support a 16k context length through inference extrapolation, which is significantly longer than the 4096 context length of many other large language models. This could enable the model to handle longer-form text generation tasks or applications that require maintaining context over longer sequences.
Updated 5/28/2024
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internlm-chat-7b
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internlm-chat-7b is a 7 billion parameter AI language model developed by InternLM, a collaboration between the Shanghai Artificial Intelligence Laboratory, SenseTime Technology, the Chinese University of Hong Kong, and Fudan University. The model was trained on a vast dataset of over 2 trillion high-quality tokens, establishing a powerful knowledge base. To enable longer input sequences and stronger reasoning capabilities, it supports an 8k context window length. Compared to other models in the 7B parameter range, InternLM-7B and InternLM-Chat-7B demonstrate significantly stronger performance across a range of benchmarks, including disciplinary competence, language competence, knowledge competence, inference competence, and comprehensive understanding. Model inputs and outputs internlm-chat-7b is a text-to-text language model that can be used for a variety of natural language processing tasks. The model takes plain text as input and generates text as output. Some key highlights include: Inputs Natural language prompts**: The model can accept a wide range of natural language prompts, from simple queries to multi-sentence instructions. Context length**: The model supports an 8k context window, allowing it to reason over longer input sequences. Outputs Natural language responses**: The model generates human-readable text responses, which can range from short phrases to multi-paragraph passages. Versatile toolset**: The model provides a flexible toolset, enabling users to build their own custom workflows and applications. Capabilities internlm-chat-7b demonstrates strong performance across a range of benchmarks, including disciplinary competence, language competence, knowledge competence, inference competence, and comprehensive understanding. For example, on the MMLU benchmark, the model achieves a score of 50.8, outperforming the LLaMA-7B, Baichuan-7B, and Alpaca-7B models. Similarly, on the AGI-Eval benchmark, the model scores 42.5, again surpassing the comparison models. What can I use it for? With its robust knowledge base, strong reasoning capabilities, and versatile toolset, internlm-chat-7b can be applied to a wide range of natural language processing tasks and applications. Some potential use cases include: Content creation**: Generate high-quality written content, such as articles, reports, and stories. Question answering**: Provide informative and well-reasoned responses to a variety of questions. Task assistance**: Help users complete tasks by understanding natural language instructions and generating relevant outputs. Conversational AI**: Engage in natural, contextual dialogues and provide helpful responses to users. Things to try One interesting aspect of internlm-chat-7b is its ability to handle longer input sequences. Try providing the model with more detailed, multi-sentence prompts and observe how it is able to leverage the extended context to generate more coherent and informative responses. Additionally, experiment with the model's versatile toolset to see how you can customize and extend its capabilities to suit your specific needs.
Updated 5/28/2024
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internlm-7b
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InternLM-7B is a 7 billion parameter large language model developed by the Shanghai Artificial Intelligence Laboratory. The model has been trained on a vast amount of high-quality data, including web text, books, and code, to establish a strong knowledge base. It provides a versatile toolset for users to build their own workflows. InternLM-7B is part of the InternLM model series, which also includes the InternLM-Chat-7B model, a version fine-tuned for conversational abilities. Compared to similar models like LLaMA-7B, Baichuan-7B, and ChatGLM2-6B, InternLM-7B demonstrates stronger performance across various benchmarks, including disciplinary competence, language competence, knowledge competence, inference competence, and comprehension competence. Model inputs and outputs Inputs Free-form text input Can handle input sequences up to 8,192 tokens in length Outputs Free-form text output Generates coherent and contextually relevant responses Capabilities InternLM-7B excels at a wide range of natural language processing tasks, including question answering, task completion, and open-ended conversation. It has shown particularly strong performance on Chinese and English language understanding, as well as reasoning and mathematical abilities. For example, on the MMLU (Multi-Task Language Understanding) benchmark, InternLM-7B achieves a score of 51.0%, outperforming models like LLaMA-7B (35.2%) and Baichuan-7B (41.5%). On the GSM8K (Grade School Math) benchmark, InternLM-7B scores 31.2%, again surpassing LLaMA-7B (10.1%) and Baichuan-7B (9.7%). What can I use it for? InternLM-7B can be used for a wide range of natural language processing applications, such as content generation, question answering, task completion, and open-ended dialogue. Its strong performance on Chinese and English language understanding and reasoning makes it a valuable tool for multilingual applications. Potential use cases include: Chatbots and virtual assistants Automated writing and content generation Language translation and multilingual support Educational and tutoring applications Research and analysis tasks requiring natural language understanding Things to try One interesting aspect of InternLM-7B is its ability to handle longer input sequences, up to 8,192 tokens, thanks to its optimized architecture. This can be particularly useful for tasks that require reasoning over longer contexts, such as summarization, question answering, or task completion over multi-step instructions. Additionally, the model's strong performance on mathematical and reasoning tasks suggests it could be a valuable tool for applications that involve quantitative analysis or problem-solving, such as financial forecasting, scientific research, or even software engineering.
Updated 5/28/2024
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internlm2-chat-20b
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internlm2-chat-20b is a 20 billion parameter language model developed by InternLM. It is an open-sourced model that has been fine-tuned for practical chat scenarios, building on InternLM's previous 7 billion parameter base model. Compared to the earlier version, internlm2-chat-20b exhibits significantly improved performance across a variety of benchmarks, including reasoning, math, code, chat experience, instruction following, and creative writing. In some evaluations, it may even match or surpass the capabilities of ChatGPT (GPT-3.5). The model's 200,000 token context window allows it to excel at long-context tasks, and it also provides strong code interpretation and data analysis capabilities. Additionally, it demonstrates an enhanced ability to utilize tools and follow multi-step instructions, enabling it to support more complex agent workflows. Model Inputs and Outputs Inputs Text input Outputs Generated text Capabilities internlm2-chat-20b has outstanding comprehensive performance, outperforming similar-sized open-source models across a range of benchmarks. It exhibits leading capabilities in areas such as reasoning, math, code, chat experience, instruction following, and creative writing. The model's 200,000 token context window allows it to excel at long-context tasks, and it also provides strong code interpretation and data analysis capabilities. What Can I Use It For? You can use internlm2-chat-20b for a variety of natural language tasks, such as: Chatbots and conversational agents**: The model's strong chat experience and instruction following abilities make it well-suited for building engaging conversational AI assistants. Content generation**: The model's capabilities in areas like creative writing and text generation can be leveraged to produce high-quality content for various applications. Problem-solving and task assistance**: The model's reasoning, math, and code interpretation skills can aid in solving complex problems and automating multi-step workflows. Data analysis**: The model's data analysis capabilities can be utilized to extract insights and generate reports from structured and unstructured data. Things to Try One interesting aspect of internlm2-chat-20b is its ability to perform well on long-context tasks, thanks to its 200,000 token context window. You can try prompting the model with long-form inputs and observe how it maintains coherence and provides relevant and insightful responses. Additionally, you can explore the model's versatility by testing its capabilities across a diverse range of domains, from creative writing to technical problem-solving.
Updated 5/28/2024
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internlm-20b
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The internlm-20b model is a 20 billion parameter pretrained language model developed by the Shanghai Artificial Intelligence Laboratory in collaboration with SenseTime Technology, the Chinese University of Hong Kong, and Fudan University. Compared to smaller models like internlm-7b and internlm-chat-7b, the internlm-20b model has a deeper architecture with 60 layers, allowing it to achieve significant improvements in understanding, reasoning, mathematical, and programming abilities. The model was trained on over 2.3 trillion tokens of high-quality English, Chinese, and code data. It also underwent SFT and RLHF training for the chat version, enabling it to better and more securely meet users' needs. On the 5 capability dimensions proposed by OpenCompass, the internlm-20b model achieved excellent results, outperforming other large models in the 13B-33B parameter range. Model Inputs and Outputs Inputs Text**: The internlm-20b model can accept text input for language modeling and generation tasks. Outputs Text**: The model generates coherent and contextual text outputs based on the input. Utility invocation**: The model has strong utility invocation capabilities, allowing it to perform various tasks like calculations, programming, and data analysis. Capabilities The internlm-20b model excels at a wide range of language tasks, including understanding, reasoning, mathematics, and programming. It achieves state-of-the-art performance on benchmark datasets like MMLU, C-Eval, and GSM8K, demonstrating its technical proficiency. The model's 16k context length also enables it to handle longer input sequences and perform stronger reasoning. What Can I Use It For? The internlm-20b model can be a valuable tool for a variety of applications, such as: Content generation**: The model can be used to generate high-quality text content, including articles, stories, and dialogue, across various domains. Question answering and knowledge retrieval**: The model's strong understanding and reasoning capabilities make it suitable for building question-answering systems and knowledge retrieval applications. Code generation and programming assistance**: The model's programming abilities allow it to assist with code generation, debugging, and software development tasks. Data analysis and visualization**: The model can be used to extract insights from data and generate visual representations of findings. Things to Try One interesting aspect of the internlm-20b model is its strong utility invocation capability. You can try prompting the model to perform various tasks like mathematical calculations, unit conversions, or even simple programming. The model's ability to understand and execute these types of instructions is a testament to its technical proficiency and versatility. Another area to explore is the model's performance on long-context tasks. Given its 16k context length, you can experiment with providing the model with extensive background information and prompts that require reasoning across a large amount of text. This can help you understand the model's strengths in handling complex, multi-faceted scenarios.
Updated 5/28/2024
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internlm2-chat-7b
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The internlm2-chat-7b model is a 7 billion parameter language model developed by internlm, a team that has also open-sourced larger models like the internlm2-chat-20b. This model is optimized for practical conversational scenarios, with capabilities that surpass other open-source models of similar size. The internlm2-chat-7b model has several key characteristics. It leverages a 200K context window, allowing it to excel at long-form tasks like LongBench and L-Eval. It also demonstrates strong performance across a variety of benchmarks, including reasoning, math, code, chat experience, instruction following, and creative writing. Notably, the internlm2-chat-20b version may even match or exceed the capabilities of ChatGPT. The model also includes a code interpreter and data analysis capabilities, providing compatible performance with GPT-4 on tasks like GSM8K and MATH. Additionally, the internlm2 series demonstrates improved tool utilization, enabling more flexible multi-step workflows for complex tasks. Model inputs and outputs Inputs Text prompts**: The internlm2-chat-7b model accepts natural language text prompts as input. Outputs Generated text**: The model outputs generated text responses based on the provided prompts. Capabilities The internlm2-chat-7b model exhibits strong performance across a range of benchmarks, including reasoning, math, code, chat experience, instruction following, and creative writing. For example, on the MATH dataset, the internlm2-chat-7b model scored 23.0, outperforming the LLaMA-7B model and approaching the performance of larger models like GPT-4. What can I use it for? The internlm2-chat-7b model can be used for a variety of language-based tasks, such as: Conversational AI**: The model's strong chat experience capabilities make it well-suited for building conversational AI assistants. Content generation**: The model's creative writing abilities allow it to generate high-quality text, such as articles, stories, or poems. Code generation and assistance**: The model's code interpreter and programming capabilities can be leveraged to assist with code-related tasks. Things to try One interesting aspect of the internlm2-chat-7b model is its ability to handle long-form contexts. You can experiment with providing the model with longer prompts or sequences of text to see how it performs on tasks that require understanding and reasoning over extended information. Additionally, you can explore the model's capabilities in areas like math, coding, and data analysis by prompting it with relevant tasks and evaluating its responses. The OpenCompass evaluation tool provides a comprehensive way to benchmark the model's performance across various domains.
Updated 5/28/2024
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internlm-xcomposer2-vl-7b
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internlm-xcomposer2-vl-7b is a vision-language large model (VLLM) based on InternLM2 for advanced text-image comprehension and composition. The model was developed by internlm, who have also released the internlm-xcomposer model for similar capabilities. internlm-xcomposer2-vl-7b achieves strong performance on various multimodal benchmarks by leveraging the powerful InternLM2 as the initialization for the language model component. Model inputs and outputs internlm-xcomposer2-vl-7b is a large multimodal model that can accept both text and image inputs. The model can generate detailed textual descriptions of images, as well as compose text and images together in creative ways. Inputs Text**: The model can take text prompts as input, such as instructions or queries about an image. Images**: The model can accept images of various resolutions and aspect ratios, up to 4K resolution. Outputs Text**: The model can generate coherent and detailed textual responses based on the input image and text prompt. Interleaved text-image compositions**: The model can create unique compositions by generating text that is interleaved with the input image. Capabilities internlm-xcomposer2-vl-7b demonstrates strong multimodal understanding and generation capabilities. It can accurately describe the contents of images, answer questions about them, and even compose new text-image combinations. The model's performance rivals or exceeds other state-of-the-art vision-language models, making it a powerful tool for tasks like image captioning, visual question answering, and creative text-image generation. What can I use it for? internlm-xcomposer2-vl-7b can be used for a variety of multimodal applications, such as: Image captioning**: Generate detailed textual descriptions of images. Visual question answering**: Answer questions about the contents of images. Text-to-image composition**: Create unique compositions by generating text that is interleaved with an input image. Multimodal content creation**: Combine text and images in creative ways for applications like advertising, education, and entertainment. The model's strong performance and efficient design make it well-suited for both academic research and commercial use cases. Things to try One interesting aspect of internlm-xcomposer2-vl-7b is its ability to handle high-resolution images at any aspect ratio. This allows the model to perceive fine-grained visual details, which can be beneficial for tasks like optical character recognition (OCR) and scene text understanding. You could try inputting images with small text or complex visual scenes to see how the model performs. Additionally, the model's strong multimodal capabilities enable interesting creative applications. You could experiment with generating text-image compositions on a variety of topics, from abstract concepts to specific scenes or narratives. The model's ability to interweave text and images in novel ways opens up possibilities for innovative multimodal content creation.
Updated 5/28/2024
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internlm-xcomposer2-4khd-7b
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internlm-xcomposer2-4khd-7b is a general vision-language large model (VLLM) based on InternLM2, with the capability of 4K resolution image understanding. It was created by internlm, who has also released similar models like internlm-xcomposer2-vl-7b, internlm-xcomposer, and internlm-7b. Model inputs and outputs internlm-xcomposer2-4khd-7b is a vision-language model that can take images and text as input, and generate relevant text as output. The model is capable of understanding and describing images in high resolution (4K) detail. Inputs Images**: The model can take 4K resolution images as input. Text**: The model can also accept text prompts or questions related to the input image. Outputs Descriptive text**: The model can generate detailed text descriptions that explain the contents and fine details of the input image. Capabilities The internlm-xcomposer2-4khd-7b model excels at understanding and describing 4K resolution images. It can analyze the visual elements of an image in depth, and provide nuanced, coherent text descriptions that capture the key details and insights. This makes the model useful for applications that require high-quality image captioning or visual question answering. What can I use it for? The internlm-xcomposer2-4khd-7b model could be useful for a variety of applications that involve processing and understanding high-resolution images, such as: Automated image captioning for marketing, e-commerce, or social media Visual question answering systems to assist users with detailed image analysis Intelligent image search and retrieval tools that can understand image content Art, design, and creative applications that require detailed image interpretation Things to try One interesting aspect of the internlm-xcomposer2-4khd-7b model is its ability to understand and describe fine visual details in high-resolution images. You could try providing the model with complex, detailed images and see how it responds, paying attention to the level of detail and nuance in the generated text. Additionally, you could experiment with using the model in multimodal applications that combine image and text inputs to explore its capabilities in areas like visual question answering or image-based storytelling.
Updated 5/28/2024
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internlm2-20b
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The internlm2-20b model is a large language model developed by the maintainer internlm. It is part of the InternLM series of models, which includes a 20B parameter base model and a chat-oriented version. The internlm2-20b model was pre-trained on over 2.3T tokens of high-quality English, Chinese, and code data, and has a deeper 60-layer architecture compared to more conventional 32 or 40 layer models. The internlm2-20b model exhibits significant improvements over previous generations, particularly in understanding, reasoning, mathematics, and programming abilities. It supports an extremely long context window of up to 200,000 characters, and has leading performance on long-context tasks like LongBench and L-Eval. The maintainer also provides a chat-oriented version, internlm2-chat-20b, that has undergone further training using supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF) to improve its conversational and task-oriented capabilities. Model Inputs and Outputs Inputs Text Sequences**: The internlm2-20b model can accept text sequences as input, with a maximum context length of 200,000 characters. Outputs Generative Text**: The model can generate fluent, coherent text in response to the input, exhibiting strong performance on a variety of language tasks. Numeric Outputs**: The model has demonstrated competence in mathematical reasoning and can provide numeric outputs for tasks like solving math problems. Code Generation**: The model can generate working code snippets and complete programming tasks. Capabilities The internlm2-20b model has shown excellent performance across a range of benchmarks, including the Multimodal Mix of Tasks and Languages (MMLU), the AGI Evaluation (AGI-Eval), and the Benchmark for Boolean Holistic Reasoning (BBH). It matches or surpasses the performance of large language models like GPT-4 on some tasks, particularly those requiring long-context understanding, mathematical reasoning, and programming abilities. What Can I Use It For? The internlm2-20b model's strong performance and versatile capabilities make it a compelling choice for a wide range of applications. Some potential use cases include: Conversational AI**: The internlm2-chat-20b version of the model is well-suited for building intelligent conversational agents that can engage in natural, context-aware dialogue. Content Generation**: The model can be used to generate high-quality written content, from articles and stories to product descriptions and marketing copy. Code Generation and Assistance**: The model's programming abilities make it useful for tasks like automatically generating code snippets, providing code explanations, and even completing programming assignments. Data Analysis and Visualization**: The model can be leveraged to analyze complex datasets, extract insights, and generate visualizations to communicate findings. Things to Try One of the most interesting aspects of the internlm2-20b model is its exceptional ability to handle long-form text. Try using the model with the LMDeploy tool to see how it performs on tasks that require understanding and reasoning over very long input sequences, such as summarizing lengthy research papers or answering questions about complex historical documents. Additionally, explore the model's versatility by tasking it with a variety of creative and analytical challenges, from generating novel story ideas to solving complex math problems. The model's strong performance across a wide range of benchmarks suggests that it may be a valuable tool for tackling diverse problems and unlocking new possibilities in AI-powered applications.
Updated 6/29/2024