Our 22 open source Gemstone models for scaling laws range from 50M to 2B parameters, spanning 11 widths from 256 to 3072 and 18 depths from 3 to 80.
Tom Goldstein's Lab at University of Maryland, College Park
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AI & ML interests
AI security & privacy, algorithmic bias, foundations of ML
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7
These are checkpoints for recurrent LLMs developed to scale test-time compute by recurring in latent space.
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tomg-group-umd/huginn-0125
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Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach
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tomg-group-umd/huginn_swa_100_10_avg_0.9_merge
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tomg-group-umd/step-00010752-recurrence_full_512_0
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tomg-group-umd/Gemstone-2560x8_cooldown
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tomg-group-umd/huginn-0125
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tomg-group-umd/Gemstone-3072x12_lr_ablation
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tomg-group-umd/Gemstone-256x23_lr_ablation
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tomg-group-umd/Gemstone-1024x28_lr_ablation
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tomg-group-umd/Gemstone-384x13_lr_ablation
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tomg-group-umd/Gemstone-2048x27_lr_ablation
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tomg-group-umd/Gemstone-512x16_lr_ablation
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tomg-group-umd/Gemstone-384x36_lr_ablation
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tomg-group-umd/Gemstone-1792x18_lr_ablation
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datasets
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tomg-group-umd/huginn-dataset
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tomg-group-umd/alpaca_cleaned_dataset_short
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tomg-group-umd/riftV1
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tomg-group-umd/fictional_qa_11-08-24_refolded_1-10-25_txt
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tomg-group-umd/fictional_qa_11-08-24_refolded_1-10-25
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tomg-group-umd/rift
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tomg-group-umd/fictional_qa_11-08-24_txt
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tomg-group-umd/fictional_qa_10-28-24
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tomg-group-umd/fictional_qa_mix_train_2024-09-15
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