Federated Co-tuning Framework for Large and Small Language Models
📰 ArXiv cs.AI
Learn how to implement a federated co-tuning framework for large and small language models to achieve mutual enhancement and improved performance
Action Steps
- Implement FedCoLLM framework using Python and TensorFlow to co-tune LLMs and SLMs
- Configure the framework to adapt to domain-specific tasks and knowledge
- Train the models using federated learning techniques to achieve mutual enhancement
- Evaluate the performance of the co-tuned models using metrics such as accuracy and F1-score
- Fine-tune the models further to optimize their performance on specific tasks
Who Needs to Know This
NLP engineers and researchers can benefit from this framework to improve the performance of their language models, while also enhancing the capabilities of smaller models
Key Insight
💡 Federated co-tuning of LLMs and SLMs can lead to mutual enhancement and improved performance on domain-specific tasks
Share This
Boost your LLMs and SLMs with FedCoLLM, a novel federated co-tuning framework! #LLMs #SLMs #FederatedLearning
Key Takeaways
Learn how to implement a federated co-tuning framework for large and small language models to achieve mutual enhancement and improved performance
Full Article
Title: Federated Co-tuning Framework for Large and Small Language Models
Abstract:
arXiv:2411.11707v3 Announce Type: replace-cross Abstract: By adapting Large Language Models (LLMs) to domain-specific tasks or enriching them with domain-specific knowledge, we can fully harness the capabilities of LLMs. Nonetheless, a gap persists in achieving simultaneous mutual enhancement between the server's LLM and the downstream clients' Small Language Models (SLMs). To address this, we propose FedCoLLM, a novel and parameter-efficient federated framework designed for co-tuning LLMs and S
Abstract:
arXiv:2411.11707v3 Announce Type: replace-cross Abstract: By adapting Large Language Models (LLMs) to domain-specific tasks or enriching them with domain-specific knowledge, we can fully harness the capabilities of LLMs. Nonetheless, a gap persists in achieving simultaneous mutual enhancement between the server's LLM and the downstream clients' Small Language Models (SLMs). To address this, we propose FedCoLLM, a novel and parameter-efficient federated framework designed for co-tuning LLMs and S
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