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Found 6 papers, 1 papers with code
FlattenQuant: Breaking Through the Inference Compute-bound for Large Language Models with Per-tensor Quantization
The 4-bit matrix multiplication introduced in the FlattenQuant method can effectively address the compute-bound caused by large matrix calculation.
Holmes: Towards Distributed Training Across Clusters with Heterogeneous NIC Environment
Large language models (LLMs) such as GPT-3, OPT, and LLaMA have demonstrated remarkable accuracy in a wide range of tasks.
Exploring Post-Training Quantization of Protein Language Models
In summary, our study introduces an innovative PTQ method for ProteinLMs, addressing specific quantization challenges and potentially leading to the development of more efficient ProteinLMs with significant implications for various protein-related applications.
AdaCoach: A Virtual Coach for Training Customer Service Agents
With the development of online business, customer service agents gradually play a crucial role as an interface between the companies and their customers.
Towards Generalized Models for Task-oriented Dialogue Modeling on Spoken Conversations
Building robust and general dialogue models for spoken conversations is challenging due to the gap in distributions of spoken and written data.
A Dialogue-based Information Extraction System for Medical Insurance Assessment
In the Chinese medical insurance industry, the assessor's role is essential and requires significant efforts to converse with the claimant.
