Hammer: Robust function-calling for on-device language models via function masking
Large language models have demonstrated impressive value in performing as autonomous
agents when equipped with external tools and API calls. Nonetheless, effectively harnessing …
agents when equipped with external tools and API calls. Nonetheless, effectively harnessing …
Less is more: Optimizing function calling for llm execution on edge devices
The advanced function-calling capabilities of foundation models open up new possibilities
for deploying agents to perform complex API tasks. However, managing large amounts of …
for deploying agents to perform complex API tasks. However, managing large amounts of …
Efficient and scalable estimation of tool representations in vector space
Recent advancements in function calling and tool use have significantly enhanced the
capabilities of large language models (LLMs) by enabling them to interact with external …
capabilities of large language models (LLMs) by enabling them to interact with external …
DroidCall: A Dataset for LLM-powered Android Intent Invocation
The growing capabilities of large language models in natural language understanding
significantly strengthen existing agentic systems. To power performant on-device mobile …
significantly strengthen existing agentic systems. To power performant on-device mobile …
Bridging Today and the Future of Humanity: AI Safety in 2024 and Beyond
S Han - arxiv preprint arxiv:2410.18114, 2024 - arxiv.org
The advancements in generative AI inevitably raise concerns about their risks and safety
implications, which, in return, catalyzes significant progress in AI safety. However, as this …
implications, which, in return, catalyzes significant progress in AI safety. However, as this …
Natural GaLore: Accelerating GaLore for memory-efficient LLM Training and Fine-tuning
A Das - arxiv preprint arxiv:2410.16029, 2024 - arxiv.org
Training LLMs presents significant memory challenges due to growing size of data, weights,
and optimizer states. Techniques such as data and model parallelism, gradient …
and optimizer states. Techniques such as data and model parallelism, gradient …
FPE-LLM: Highly Intelligent Time-Series Forecasting and Language Interaction LLM in Energy Systems
Z Qiu, C Li, Z Wang, H Mo, R **e, G Chen… - arxiv preprint arxiv …, 2024 - arxiv.org
This paper introduces Fusion PEFT Energy LLM (FPE-LLM), a large language model (LLM)
fine-tuned for energy system forecasting using a combination of Prefix and Lora Parameter …
fine-tuned for energy system forecasting using a combination of Prefix and Lora Parameter …