FLM-101B: An Exceptionally Affordable 101B-Scale Language Model That Rivals Top AI Models
In Brief
The Chinese LLM, FLM-101B, can be developed with a budget of just $100K, achieving outcomes that can compete with well-regarded models like GPT-3 and GLM-130B.
Researchers in China have launched a new LLM, the FLM-101B , a decoder-only LLM with a noteworthy count of 101 billion parameters. This innovation serves as a cost-effective option for both academic research and practical use.
What sets FLM-101B apart is its remarkable performance achieved with a surprisingly modest financial outlay. Although developing large language models from scratch typically involves exorbitant costs, the FLM-101B team has successfully demonstrated that a model with 101 billion parameters can indeed be trained using only a $100K budget.
The results from experiments are truly remarkable. The FLM-101B has shown performance metrics that are on par with established and resource-heavy models. models like GPT-3 and GLM-130B. This comparison underscores the extraordinary capabilities of this budget-friendly model, especially in IQ tests featuring intricate contexts absent from the training data.
In a bold step reflecting their dedication to pushing the boundaries of AI development, the creators of FLM-101B have opted to make their model open-source. This means that researchers and developers around the globe can now access and utilize this 101B-scale LLM for a diversity of applications in both Chinese and English.
The training methodology behind FLM-101B is quite unique. Initially, it assimilates knowledge from a smaller 16-billion-parameter model before gradually expanding to encompass 101 billion parameters. This progressive training strategy notably reduces costs, making it achievable for a wider array of projects.
A notable aspect of FLM-101B is its capability to facilitate efficient window size expansion during inference. This is enabled by the innovative xPos rotary position embedding, which enhances the model’s ability to consider broader contexts, thus improving its flexibility and usability.
FLM-101B's training was conducted on a cluster consisting of 24 DGX-A800 GPU servers, completing the process in under 26 days. This achievement highlights both the model’s scalability and its effective use of resources. Soon, the model's training codebase, adapted from Megatron-LM, will be made available as open-source, providing significant insights for those in the AI field.
The developers of FLM-101B are aware of certain constraints, such as the possible inclusion of unsafe examples in the training dataset due to its open-source nature. This serves as a crucial reminder about the necessity for responsible AI implementation. content moderation .
Despite FLM-101B's impressive performance, its creators admit that there is room for refinement. Although its inference capabilities are strong, they are not yet fully optimized, which results in higher resource consumption and slower processing times. Plans are in place to introduce Flash Attention during inference to rectify this issue.
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