Browsing: LLMs

LLMs have taken the world by storm. People are mostly using polished APIs of LLMs, they type a prompt, get an answer. What they miss is…

When running LLMs at scale, the real limitation is GPU memory rather than compute, mainly because each request requires a KV cache to store token-level data.…

is not a data quality problem. It is not a training problem. It is not a problem you can solve with more RLHF, better filtering, or a…

, we’ve talked a lot about what an incredible tool RAG is for leveraging the power of AI on custom data. But, whether we are talking…

In recent times, many developments in the agent ecosystem have focused on enabling AI agents to interact with external tools and access domain-specific knowledge more effectively.…

Google has officially released Android Bench, a new leaderboard and evaluation framework designed to measure how Large Language Models (LLMs) perform specifically on Android development tasks.…

def executor_agent(step: Dict[str, Any], context: Dict[str, Any]) -> StepResult: step_id = int(step.get(“id”, 0)) title = step.get(“title”, f”Step {step_id}”) tool = step.get(“tool”, “llm”) ctx_compact = { “goal”:…

Customizing Large Language Models (LLMs) currently presents a significant engineering trade-off between the flexibility of In-Context Learning (ICL) and the efficiency of Context Distillation (CD) or…

The generative AI race has long been a game of ‘bigger is better.’ But as the industry hits the limits of power consumption and memory bottlenecks,…

Introduction are currently living in a time where Artificial Intelligence, especially Large Language models like ChatGPT, have been deeply integrated into our daily lives and workflows.…