Books from the Survivor Library (mostly ~1920s & earlier) OCR'd with recent VLMs
BEEspoke Data
community
AI & ML interests
'an LLM is only as good as the dataset it was trained on' - Sun Tzu
smol_llama 220M fine-tunes we did
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BEE-spoke-data/smol_llama-220M-openhermes
Text Generation ā¢ 0.2B ā¢ Updated ā¢ 1.07k ā¢ 5 -
BEE-spoke-data/smol_llama-220M-open_instruct
Text Generation ā¢ 0.2B ā¢ Updated ā¢ 20 ā¢ 2 -
BEE-spoke-data/beecoder-220M-python
Text Generation ā¢ 0.2B ā¢ Updated ā¢ 29 ā¢ 3 -
BEE-spoke-data/zephyr-220m-sft-full
Text Generation ā¢ 0.2B ā¢ Updated ā¢ 1k ā¢ 1
models fine-tuned to be knowledgeable about apiary practice
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BEE-spoke-data/TinyLlama-3T-1.1bee
Text Generation ā¢ 1B ā¢ Updated ā¢ 29 ā¢ 2 -
BEE-spoke-data/TinyLlama-1.1bee
Text Generation ā¢ 1B ā¢ Updated ā¢ 15 ā¢ 1 -
BEE-spoke-data/Meta-Llama-3-8Bee
Text Generation ā¢ 8B ā¢ Updated ā¢ 28 -
BEE-spoke-data/phi-1bee5
Text Generation ā¢ 1B ā¢ Updated ā¢ 11 ā¢ 1
trained and adapted tokenizers - various
š§"raw" pretrained smol_llama checkpoints - WIP š§
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BEE-spoke-data/smol_llama-101M-GQA
Text Generation ā¢ 0.1B ā¢ Updated ā¢ 4.07k ā¢ 30 -
BEE-spoke-data/smol_llama-81M-tied
Text Generation ā¢ 81.3M ā¢ Updated ā¢ 1.08k ā¢ 9 -
BEE-spoke-data/smol_llama-220M-GQA
Text Generation ā¢ 0.2B ā¢ Updated ā¢ 3.04k ā¢ 13 -
BEE-spoke-data/verysmol_llama-v11-KIx2
Text Generation ā¢ 58.1M ā¢ Updated ā¢ 1.07k ā¢ 4
Pretrained encoder (fill-mask) models we made
text classification models for book genres
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BEE-spoke-data/albert-xxlarge-v2-description2genre
Text Classification ā¢ 0.2B ā¢ Updated ā¢ 15 ā¢ 2 -
BEE-spoke-data/mobilebert-uncased-title2genre
Text Classification ā¢ 24.6M ā¢ Updated ā¢ 27 ā¢ 1 -
BEE-spoke-data/roberta-large-title2genre
Text Classification ā¢ 0.4B ā¢ Updated ā¢ 11 ā¢ 1 -
BEE-spoke-data/roberta-base-description2genre
Text Classification ā¢ 0.1B ā¢ Updated ā¢ 17
concept datasets extracted from fineweb
Books from the Survivor Library (mostly ~1920s & earlier) OCR'd with recent VLMs
š§"raw" pretrained smol_llama checkpoints - WIP š§
-
BEE-spoke-data/smol_llama-101M-GQA
Text Generation ā¢ 0.1B ā¢ Updated ā¢ 4.07k ā¢ 30 -
BEE-spoke-data/smol_llama-81M-tied
Text Generation ā¢ 81.3M ā¢ Updated ā¢ 1.08k ā¢ 9 -
BEE-spoke-data/smol_llama-220M-GQA
Text Generation ā¢ 0.2B ā¢ Updated ā¢ 3.04k ā¢ 13 -
BEE-spoke-data/verysmol_llama-v11-KIx2
Text Generation ā¢ 58.1M ā¢ Updated ā¢ 1.07k ā¢ 4
smol_llama 220M fine-tunes we did
-
BEE-spoke-data/smol_llama-220M-openhermes
Text Generation ā¢ 0.2B ā¢ Updated ā¢ 1.07k ā¢ 5 -
BEE-spoke-data/smol_llama-220M-open_instruct
Text Generation ā¢ 0.2B ā¢ Updated ā¢ 20 ā¢ 2 -
BEE-spoke-data/beecoder-220M-python
Text Generation ā¢ 0.2B ā¢ Updated ā¢ 29 ā¢ 3 -
BEE-spoke-data/zephyr-220m-sft-full
Text Generation ā¢ 0.2B ā¢ Updated ā¢ 1k ā¢ 1
Pretrained encoder (fill-mask) models we made
models fine-tuned to be knowledgeable about apiary practice
-
BEE-spoke-data/TinyLlama-3T-1.1bee
Text Generation ā¢ 1B ā¢ Updated ā¢ 29 ā¢ 2 -
BEE-spoke-data/TinyLlama-1.1bee
Text Generation ā¢ 1B ā¢ Updated ā¢ 15 ā¢ 1 -
BEE-spoke-data/Meta-Llama-3-8Bee
Text Generation ā¢ 8B ā¢ Updated ā¢ 28 -
BEE-spoke-data/phi-1bee5
Text Generation ā¢ 1B ā¢ Updated ā¢ 11 ā¢ 1
text classification models for book genres
-
BEE-spoke-data/albert-xxlarge-v2-description2genre
Text Classification ā¢ 0.2B ā¢ Updated ā¢ 15 ā¢ 2 -
BEE-spoke-data/mobilebert-uncased-title2genre
Text Classification ā¢ 24.6M ā¢ Updated ā¢ 27 ā¢ 1 -
BEE-spoke-data/roberta-large-title2genre
Text Classification ā¢ 0.4B ā¢ Updated ā¢ 11 ā¢ 1 -
BEE-spoke-data/roberta-base-description2genre
Text Classification ā¢ 0.1B ā¢ Updated ā¢ 17
trained and adapted tokenizers - various
concept datasets extracted from fineweb