tinygrad,simple,educational
tinygrad is simple deep learning framework by George Hotz. Educational, hackable.
653 technical terms and definitions
tinygrad is simple deep learning framework by George Hotz. Educational, hackable.
TinyLlama is 1.1B model trained on 3T tokens. Very efficient.
Machine learning on microcontrollers.
Nanoscale Raman with AFM tip.
Critical spacing between line ends.
Older silicide technology.
Titration determines chemical concentration by measuring neutralization volume.
Defect localization using laser heating.
Thermally-Induced Voltage Alteration uses localized heating to modulate pn junction characteristics revealing resistive opens and other defects.
Fast ESD test method.
Tetramethylammonium hydroxide alternative to KOH.
Measure of organic contamination in water.
Total Organic Carbon analysis quantifies organic contamination in water.
Chemical imaging with SIMS.
Together AI provides LLM APIs and fine-tuning. Competitive pricing. Open model hosting.
Token bucket algorithm allows bursts while maintaining average rate limits.
Token budgets limit context length for cost and latency management.
Maximum number of tokens an LLM can process or generate in a single request or conversation turn.
Gather expert outputs.
Remove tokens and predict deletions.
Send tokens to appropriate experts.
Handle overflow when experts full.
Token dropping discards excess tokens when expert capacity is exceeded.
Skip processing some tokens to save compute.
Token forcing mandates specific tokens at designated positions.
Fix tokenization artifacts.
Assign importance scores to determine computation allocation.
Assign labels to patches.
Assign pseudo-labels to tokens.
Maximum prompt length.
How to combine tokens.
Combine similar tokens to reduce sequence length.
Remove unimportant tokens.
Token streaming sends individual tokens immediately rather than waiting for completion.
Search over possible continuations.
Training tokens per parameter.
Token: Subword unit. GPT uses BPE tokenization. ~1 token = 4 characters. Models predict probability of next token.
Problems from subword tokenization.
Ensure consistent tokenization.
Preprocessing before tokenization.
Protect tokens from injection or manipulation.
Tokenization splits text into tokens. BPE, SentencePiece, tiktoken. Vocabulary size trade-off (32K-100K typical).
Split text into tokens (subwords characters) for model input.
Create tokenizer vocabulary.
Tokenizer splits text into tokens. BPE/WordPiece/SentencePiece trade off vocabulary size vs. sequence length and handle multilingual text differently.
Tokenizers library provides fast tokenization. Rust implementation. Hugging Face.
Language model training speed.
Tolerance design balances specification tightness with cost and capability.
Allowed variation from target.
Component standing on end.