internlm2_5-20b-chat
Maintainer: internlm
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model Overview
internlm2_5-20b-chat is a large language model developed by internlm that has been open-sourced. It is a 20 billion parameter model that has been tailored for practical chatbot scenarios. The model has several key characteristics:
- Outstanding Reasoning Capability: The model achieves state-of-the-art performance on math reasoning tasks, surpassing models like Llama3 and Gemma2-27B.
- Stronger Tool Use:
internlm2_5-20b-chatsupports gathering information from over 100 web pages, with better instruction following, tool selection, and reflection capabilities. This is demonstrated in the examples.
Similar models include the internlm2_5-7b-chat and internlm2_5-7b-chat-1m versions, which offer different model sizes and capabilities.
Model Inputs and Outputs
internlm2_5-20b-chat is a text-to-text model, taking natural language prompts as input and generating relevant text responses. The model is designed for open-ended conversational interactions, with the ability to engage in tasks like answering questions, providing suggestions, and carrying on multi-turn dialogues.
Inputs
- Natural language prompts and questions
Outputs
- Coherent, contextually appropriate text responses
Capabilities
The model's key strengths lie in its reasoning and task-completion abilities. internlm2_5-20b-chat has demonstrated state-of-the-art performance on a range of benchmarks, including math reasoning, general knowledge, and language understanding. It can engage in substantive conversations, provide detailed explanations, and assist with complex multi-step tasks.
What Can I Use It For?
internlm2_5-20b-chat is well-suited for a variety of conversational AI applications, such as virtual assistants, chatbots, and dialogue systems. Its strong reasoning and task-completion skills make it useful for applications that require engaging with users in open-ended interactions, answering questions, providing recommendations, and helping with information-gathering and problem-solving.
Things to Try
Some interesting things to explore with internlm2_5-20b-chat include:
- Engaging the model in multi-turn dialogues to see how it maintains context and responds coherently
- Probing its reasoning and problem-solving abilities by posing math, science, or coding challenges
- Assessing its versatility by asking it to complete a variety of tasks, from creative writing to data analysis
- Experimenting with the model's tool-usage capabilities, as demonstrated in the Lagent examples
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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internlm2_5-7b-chat
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The internlm2-5-7b-chat model is a 7 billion parameter language model developed by internlm. It is part of the InternLM family of models, which also includes the internlm2-chat-7b and internlm-chat-7b models. The InternLM models are known for their outstanding reasoning capabilities, long-context support, and stronger tool use abilities compared to other open-source models of similar size. The internlm2-5-7b-chat model specifically demonstrates state-of-the-art performance on math reasoning tasks, surpassing models like LLaMA-3 and Gemma2-9B. It also excels at finding relevant information in long, 1 million character contexts, as shown by its leading results on the LongBench benchmark. Additionally, the model supports gathering information from over 100 web pages, with the corresponding implementation to be released in the Lagent project soon. Model inputs and outputs Inputs Natural language text prompts for the model to generate a response to. Outputs Generated natural language text responses to the input prompts. Capabilities The internlm2-5-7b-chat model showcases several advanced capabilities. It demonstrates outstanding reasoning skills, particularly in mathematical tasks, outperforming larger models like LLaMA-3 and Gemma2-9B. The model also has an exceptional ability to process long input contexts of up to 1 million characters, making it highly effective at "finding needles in haystacks" for tasks that require gathering and synthesizing information from large amounts of text. Additionally, the internlm2-5-7b-chat model has stronger tool use abilities compared to other open-source models. It can leverage over 100 web pages to gather information, and the upcoming Lagent project will further expand its tool utilization capabilities for complex, multi-step tasks. What can I use it for? The internlm2-5-7b-chat model's advanced reasoning, long-context, and tool use capabilities make it well-suited for a variety of applications, such as: Answering complex, multi-part questions that require gathering and synthesizing information from large amounts of text Solving challenging mathematical and logical problems Assisting with research and analysis tasks that involve sifting through large volumes of information Developing intelligent virtual assistants and chatbots with sophisticated language understanding and reasoning abilities Things to try One key aspect to explore with the internlm2-5-7b-chat model is its impressive ability to process and reason over long input contexts. Try providing the model with prompts that require it to draw insights and connections from extensive amounts of text, and observe how it is able to efficiently locate and integrate relevant information to formulate a coherent response. Another intriguing area to investigate is the model's evolving tool use capabilities. As the Lagent project progresses, experiment with prompts that involve the model leveraging various tools and data sources to tackle complex, multi-step tasks. This will help uncover the model's potential to serve as a versatile and adaptable assistant for a wide range of applications.
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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.
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internlm2_5-7b-chat-1m
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internlm2_5-7b-chat-1m is a 7 billion parameter language model developed by InternLM that has been optimized for long-context task performance. It is capable of accurately locating key information in documents up to 1 million tokens in length, outperforming other models like LLaMA3 and Gemma2-9B on long-context tasks like LongBench. The model also has stronger tool utilization capabilities, allowing it to gather information from over 100 web pages and perform better at instruction following, tool selection, and reflection. Model inputs and outputs internlm2_5-7b-chat-1m is a text-to-text model that can be used for a variety of natural language processing tasks. It takes text as input and generates text as output. Inputs Arbitrary text, such as sentences, paragraphs, or longer documents Outputs Generated text based on the input, which can be used for tasks like summarization, question answering, or open-ended dialog Capabilities internlm2_5-7b-chat-1m has outstanding reasoning and long-context comprehension capabilities. It achieves state-of-the-art performance on math reasoning tasks, surpassing models like LLaMA3 and Gemma2-9B. The model's 1 million token context window allows it to accurately locate key information in very long documents, making it a powerful tool for tasks that require deep understanding of large amounts of text. What can I use it for? internlm2_5-7b-chat-1m can be used for a variety of natural language processing tasks, such as summarization, question answering, and open-ended dialog. Its long-context comprehension capabilities make it particularly well-suited for tasks that require understanding and reasoning over large amounts of information, like research, analysis, or creative writing. The model's tool utilization abilities also allow it to be used in more complex, multi-step workflows. Things to try One interesting thing to try with internlm2_5-7b-chat-1m is its ability to handle very long input contexts. You can use the LMDeploy toolkit to perform inference with 1 million token contexts and see how the model performs on tasks that require understanding and reasoning over large amounts of information. Another idea is to explore the model's tool utilization capabilities by trying the Lagent implementation, which allows the model to gather information from multiple web sources to assist with complex tasks.
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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.
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