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antalvdb/
olifant-speculative-ibases

Name: olifant-speculative-ibases
Availability: InStock

speculative-decoding

memory-based-learning

timbl

energy-efficiency

apache-2.0

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Olifant instance bases for speculative decoding (INLG 2026)

Four trained TiMBL IGTree instance bases used as speculative-decoding draft models in:

Antal van den Bosch. 2026. Towards Green Text Generation: Memory-Based Speculative Decoding. In Proceedings of the International Natural Language Generation Conference (INLG 2026). To appear.

Code: https://github.com/antalvdb/olifant-speculative

Directory	Training corpus	Tokens	Ibase size
`olifant-fineweb`	FineWeb-Edu (1 shard)	1.089B	4.2 GB
`olifant-pubmed`	PubMed Summarization train	536M	1.1 GB
`olifant-wikitext`	WikiText-103 train	130M	547 MB
`olifant-eurlex`	LexGLUE EurLex train	94M	138 MB

All models: IGTree (-a1), context window 4, tokenized with mistralai/Mistral-7B-v0.1 SentencePiece, no class distributions (+D off). Each directory holds the .ibase, its .wgt weights file, and an olifant_config.json recording tokenizer, window size, and algorithm.

Usage: download a directory into the models/ folder of the olifant-speculative repo and pass it to the benchmark runners. Loading is done by timblserver (one-time, up to ~1 minute for the largest ibase); queries then take well under a millisecond on CPU.

Training corpora are used under their respective terms (FineWeb-Edu ODC-By, WikiText CC BY-SA, PubMed and LexGLUE per their distributions). The instance bases store 4-token context windows with next-token labels derived from these corpora.

antalvdb/olifant-speculative-ibases

Olifant instance bases for speculative decoding (INLG 2026)

antalvdb/
olifant-speculative-ibases