token forcing, llm optimization
Token forcing mandates specific tokens at designated positions.
189 technical terms and definitions
Token forcing mandates specific tokens at designated positions.
Assign importance scores to determine computation allocation.
Maximum prompt length.
Combine similar tokens to reduce sequence length.
Token streaming sends individual tokens immediately rather than waiting for completion.
Training tokens per parameter.
Create tokenizer vocabulary.
Percentage of time tool is ready.
Tool calling agents invoke external functions APIs or resources to accomplish tasks.
Validate tool call arguments before execution.
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.
Tool result parsing extracts relevant information from function outputs for agent reasoning.
Tool selection chooses appropriate functions from available repertoire for current needs.
LLM decides when and how to call external APIs tools or functions.
Teach models to use external tools.
Equip models with calculators search APIs code execution.
ToolBench evaluates agent ability to use diverse APIs and tools effectively.
Model trained to decide when and how to use tools.
Top-k routing selects k highest-scoring experts for each token.
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.
Consider network structure.
TorchScript creates serializable and optimizable representations of PyTorch models.
Total cost of ownership includes purchase price plus logistics inventory quality and risk costs.
Holistic maintenance approach.
Model trained to detect harmful language.
Identify toxic content.
Toxicity detection identifies offensive abusive or hateful language in text.
Classify text for hate speech offensive language or harmful content.
Predict toxic effects of compounds.
Efficient influence computation.
Balance accuracy and robustness.
Older larger process nodes still used for cost-sensitive products.
Trailing-edge nodes are mature processes offering stability and cost advantages.
Total FLOPS for training.
Predict computational cost.
Total compute and time required to train a model.
Training data attribution identifies which training examples most influenced specific predictions.
Attempt to extract memorized training examples from model.
Tradeoff between data aspects.
Measure computational efficiency.
Manage complex training workflows.
Massive-scale distributed training.
Optimize end-to-end training workflow.
Estimate training duration.
Training verification confirms personnel understand and can execute procedures.