SINGAPORE, Oct. 1, 2023 /PRNewswire/ -- Recently, Colossal-AI built a remarkable domain-specific large language model (LLM) by utilizing only a few hundred dollars training costs. It can be readily applied across various domains, facilitating the economical construction of large AI models.
The solution is accessible without any commercial restrictions, with complete transparency extended to the entire training process, code, and model weights.
Technical details, open-source code and weights are available at: https://github.com/hpcaitech/ColossalAI
Performance
Colossal-AI's model not only enhances Chinese language capabilities but also further refines its proficiency in English. Remarkably, it exhibits performance levels that rival state-of-the-art (SOTA) models of similar scale within the open-source community.
In conjunction with this, Colossal-AI offers the comprehensive evaluation framework, ColossalEval, facilitating cost-effective reproducibility.
In addition, fine-tuning through methods like SFT and LoRA has limitations in effectively infusing knowledge and capabilities from the base model. It doesn't satisfactorily meet the requirements for constructing high-quality domain-specific knowledge or specialized model applications.
Bridging from General Large Models to Domain-specific Large Models
More importantly, the creation of a Chinese version not only offers the advantage of reusability but also carries significant importance in real-world implementation scenarios.
It's widely recognized that the cost of pre-training large AI models from scratch is exorbitant, often humorously referred to as a domain accessible only to those with "50 million dollars" to spare.
Many tech giants and AI startups are eager to invest heavily in building large general-purpose models. However, behind the generality of these large models often lies a lack of domain-specific knowledge. Consequently, the issue of practical applicability becomes particularly serious.
If a domain-specific large model can be rapidly and cost-effectively constructed, followed by fine-tuning for specific business needs, it would undoubtedly advance the deployment of applications, providing a competitive advantage.
Applying the above process to perform knowledge transfer in any field allows for the cost-effective construction of lightweight domain-specific foundational large models.
For constructing foundational large models from scratch, one can also draw inspiration from the aforementioned experiences and Colossal-AI's cost-reduction and efficiency-enhancing capabilities to efficiently achieve this goal at minimal cost.
Colossal-AI System Optimization and Cloud Platform
The impressive performance and cost advantages are built upon the foundation of the low-cost AI large model development system, Colossal-AI.
Colossal-AI leverages efficient techniques to reduce the costs of large AI models training, fine-tuning, and inference. It has collaborated with numerous Fortune 500 companies and other well-known enterprises.
To further enhance the efficiency of large model development and deployment, Colossal-AI has been upgraded to the Colossal-AI cloud platform, which is now in public beta, and registration will provide you with vouchers.
Colossal-AI Cloud Platform: platform.colossalai.com
Colossal-AI Open Source Address: https://github.com/hpcaitech/ColossalAI
About HPC-AI Tech
HPC-AI Tech is a startup headquartered in Singapore. Its flagship product, Colossal-AI, is a versatile deep learning system designed for the era of large AI models. It enables efficient and rapid deployment of large AI model training and inference, resulting in significant cost reduction for large AI model applications. HPC-AI Tech raised 22 million USD in Series A Funding in July 2023.
For media inquiries or more information, please contact:
contact@hpc-ai.tech
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