internlm-chat-20b
Maintainer: internlm
136
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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
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.
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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