tool availability,production
Percentage of time tool is ready.
653 technical terms and definitions
Percentage of time tool is ready.
Tool calling agents invoke external functions APIs or resources to accomplish tasks.
Validate tool call arguments before execution.
Ensure sufficient equipment.
Contamination from equipment.
Tool discovery enables agents to find and learn about available functions dynamically.
Tool documentation describes function capabilities parameters and expected outputs for agent understanding.
Tool idle management powers down unused equipment components reducing standby energy consumption.
Keep tools similar.
Detailed capability requirements.
Verify tool performance post-maintenance.
Validate equipment meets requirements.
Tool result parsing extracts relevant information from function outputs for agent reasoning.
Tool selection chooses appropriate functions from available repertoire for current needs.
Choose appropriate tool.
Chain multiple tools.
Group of tools performing same process step.
LLM decides when and how to call external APIs tools or functions.
Assess tool-using capabilities.
Teach models to use external tools.
Tool use enables language models to invoke external functions APIs or search engines.
Maximize productive time.
Stabilization period after maintenance.
Equip models with calculators search APIs code execution.
Variation from equipment.
Consistency across different tools.
Tool-to-tool variation measures performance differences between nominally identical equipment.
ToolBench evaluates agent ability to use diverse APIs and tools effectively.
Benchmark for tool use.
Model trained to decide when and how to use tools.
Toolformer learns to use tools. Self-supervised tool learning. Meta research.
Top-k sampling limits to k highest probability tokens, then samples. Fixed vocabulary cutoff.
Markings on package top.
Route to two best experts.
SEM imaging from above to measure CD and patterns.
Route each token to k best experts.
Send only k largest gradients.
Return K most relevant documents.
Top-k routing selects k highest-scoring experts for each token.
Sample from K most likely tokens.
Sample only from the k most probable next tokens.
Sample from smallest set of tokens whose cumulative probability exceeds p.
Sample from smallest set with cumulative prob P.
Topic restrictions keep model on-task. Refuse off-topic queries politely. Focus assistant.
Top-K pooling selects a fixed number of highest-scoring nodes based on learned projection vectors for hierarchical graph representation.
Select top-k nodes by importance.
Qubits protected by topology.
Find optimal material distribution.
Consider network structure.
torch.compile uses Inductor to JIT-compile models. Automatic optimizations. 2x speedup on many workloads.