internlm-chat-7b

Maintainer: internlm

Total Score

99

Last updated 5/21/2024

👨‍🏫

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

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.



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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