LFM3-1.7B-abliterated
bowtiedrahman基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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Qwen3.8-Flash-Next-IQ3_S-GGUF
boxrunnerA GGUF quantization of Qwen/Qwen3.8-Flash-Next, a 512-expert mixture-of-experts model with a 262 144-token context and image input. It averages 4.081 bits per weight. It is the same file as boxrunner/Qwen3.8-Flash-Next-mixC-GGUF (map C) with one change: the routed experts' gate and up projections are **IQ3_S** instead of IQ2_XXS. Every other tensor is byte-identical to map C. Routed-expert down is IQ4_NL, the n-gram table is IQ4_NL, and the rest is Q5_K, Q6_K and Q8_0. All 24 576 experts together take 53.3 GiB, so on a 64 GB Mac they do not all fit in memory at once. Map C's do (40.4 GiB). The two parts were cut with llama.cpp's `gguf-split`. Put them in one folder and open part 1; the engine finds part 2 itself. `llama-gguf-split --merge` turns them back into one file. The projector and the draft head are the same files as in the map C repository.
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View details ↗Qwen3.8-Flash-Next-mixC-GGUF
boxrunner基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qwen3-4B-AgentCoder
br1-pist基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗controlnet-union-sdxl-1.0-promax
brad-twinkl基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Mmojo-Server
bradhutchings基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗reve-base
brain-bzhREVE (project page here) is a transformer-based foundation model for EEG signal processing. It was trained on 60k hours of EEG data from various sources and is designed to be adaptable to any electrode configuration and a wide range of EEG-based tasks. REVE (Representation for EEG with Versatile Embeddings), a pretrained encoder explicitly designed to generalize across diverse EEG signals. REVE introduces a novel 4D positional encoding scheme that enables it to process signals of arbitrary length and electrode arrangement. Using a masked autoencoding objective, we pretrain REVE on over 60,000 hours of EEG data from 92 datasets spanning 25,000 subjects. **Developed by** the BRAIN team and UdeM
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View details ↗reve-large
brain-bzh基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗reve-positions
brain-bzhWrapper to provide electrode positions to use the REVE EEG Foundation Model. - **Developed by:** the BRAIN team and UdeM - **Funded by :** AI@IMT, ANR JCJC ENDIVE, Jean Zay (with project numbers), Alliance Canada and Region Bretagne. REVE (Representation for EEG with Versatile Embeddings) is a pretrained model explicitly designed to generalize across diverse EEG signals. REVE introduces a novel 4D positional encoding scheme that enables it to process signals of arbitrary length and electrode arrangement.
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View details ↗gpt2-babylm-3
BrainAlign基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗gpt2-babylm-5
BrainAlign基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗gpt2-babylm-7
BrainAlign基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗gpt2-babylm-9
BrainAlign基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗cyber-analyst-4B
BrainboxAI基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗BaRISTA-coords
braindecode基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗biot-pretrained-prest-16chs
braindecode基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗biot-pretrained-shhs-prest-18chs
braindecode基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗biot-pretrained-six-datasets-18chs
braindecodeThis model has been pushed to the Hub using the PytorchModelHubMixin integration: - Code: https://braindecode.org - Paper: [More Information Needed] - Docs: https://braindecode.org/stable/generated/braindecode.models.BIOT.html
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View details ↗brainbert-pretrained
braindecodePretrained weights for `braindecode.models.BrainBERT`, a faithful braindecode port of **BrainBERT** (Wang et al., ICLR 2023), a self-supervised foundation model for intracranial (sEEG/iEEG) recordings. These weights are the **official pretrained "large" checkpoint** (`stft` variant) released by the original authors. The Transformer encoder and input encoding are mapped **1:1** into the braindecode `BrainBERT` module. This repository ships the **encoder only** — 82 tensors, bit-identical to the authors' release. No downstream head is included: BrainBERT is pretrained by masked spectrogram reconstruction and has no classification head to inherit, so `final_layer` is freshly initialized by `from_pretrained` for whatever `n_outputs` you ask for.
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View details ↗braindecode-bendr
braindecodeThis model has been pushed to the Hub using the PytorchModelHubMixin integration: - Code: https://braindecode.org - Paper: [More Information Needed] - Docs: https://braindecode.org/stable/generated/braindecode.models.BENDR.html
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View details ↗brainomni-base-pretrained
braindecode基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗brant-pretrained
braindecode基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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