FLM-101B

Maintainer: CofeAI

Total Score

87

Last updated 5/28/2024

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

The FLM-101B is a 101 billion parameter decoder-only language model developed by CofeAI. It was trained using a technique called "model growth", which involves rapidly acquiring knowledge on a smaller 16B model before gradually scaling up to the full 101B size. This approach is said to be cost-effective, costing approximately $100,000 to train.

The model supports both Chinese and English languages, with a context window length of 2048 during training. It utilizes xPos rotary position embedding, which allows for efficient expansion of the window size during inference.

Compared to similar large language models like Baichuan-7B, DeciLM-6B, and InternLM-20B, the FLM-101B stands out as the largest known model trained with xPos, the largest that successfully implements p transfer and loss prediction, and the largest to use progressive learning with model growth.

Model inputs and outputs

Inputs

  • Free-form text in either Chinese or English

Outputs

  • Continuation of the input text, generated in an autoregressive manner

Capabilities

The FLM-101B model has demonstrated strong performance on a variety of language tasks, including open-ended text generation, question answering, and code generation. Its large scale and bilingual capabilities make it a powerful tool for applications that require reasoning, understanding, and generation across multiple languages.

What can I use it for?

The FLM-101B model can be used for a wide range of applications, such as:

  • Generating high-quality content in Chinese and English (e.g., articles, stories, dialogues)
  • Powering multilingual chatbots and virtual assistants
  • Providing a strong base for fine-tuning on specialized tasks (e.g., text summarization, machine translation)
  • Enhancing language understanding and generation in multilingual AI systems

Things to try

One interesting aspect of the FLM-101B model is its use of the xPos rotary position embedding technique. This approach allows the model to efficiently handle longer input sequences during inference, beyond the 2048 context window used during training. Experimenting with the model's performance on tasks that require long-range dependencies or contextual understanding could yield valuable insights.

Additionally, the model's bilingual capabilities open up the possibility of exploring cross-lingual transfer learning, where the model's knowledge in one language is leveraged to improve performance on tasks in the other language. Comparing the model's performance on monolingual and multilingual benchmarks could provide useful information about its strengths and limitations.



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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