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nietzsche-gpt-15m
LyNxFliX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 ↗micro-qwen4exp
Lynxpda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 ↗AuriAetherwiing_G4-26B-A4B-Musica-v1-GGUF
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View details ↗AuriAetherwiing_G4-E4B-Musica-v1-GGUF
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View details ↗AuriAetherwiing_TQ3.5-27B-Musica-v1-GGUF
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View details ↗Stellar-Odyssey-12b-v0.0
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View details ↗arxiv-nlp
lysandre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 ↗CLIP-ViT-L-14-laion2B-s32B-b82K
lysandre1. Model Details 2. Uses 3. Training Details 4. Evaluation 5. Acknowledgements 6. Citation 7. How To Get Started With the Model A CLIP ViT L/14 model trained with the LAION-2B English subset of LAION-5B (https://laion.ai/blog/laion-5b/) using OpenCLIP (https://github.com/mlfoundations/open_clip). Model training ('babysitting') done by Ross Wightman on the JUWELS Booster supercomputer. See acknowledgements below.
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View details ↗test-simple-tagger-tiny
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View details ↗tiny-bert-random
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View details ↗tiny-electra-random
lysandre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 ↗tiny-tapas-random-sqa
lysandre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 ↗tiny-tapas-random-wtq
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View details ↗tiny-vit-random
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View details ↗GLM-5.2-Q2_K-MTP-Q8-layers
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View details ↗Inkling-Small-GGUF
lyssquant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 ↗Inkling-Small-UD-Q4_K_XL-layers
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View details ↗inkling-UD-Q2_K_XL-layers
lyssquant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 ↗The model is based on DeBERTa-v3-Large that predicts a binary label - 1 for supported and 0 for unsupported. The model is doing predictions on the *sentence-level*. It takes as input a document and a sentence and determine whether the sentence is supported by the document: **MiniCheck-Model(document, claim) -> {0, 1}** MiniCheck-DeBERTa-v3-Large is fine tuned from `microsoft/deberta-v3-large` (He et al., 2023) on the combination of 35K data: - 21K ANLI data (Nie et al., 2020) - 14K synthetic data generated from scratch in a structed way (more details in the paper). The performance of these models is evaluated on our new collected benchmark (unseen by our models during training), LLM-AggreFact, from 11 recent human annotated datasets on fact-checking and grounding LLM generations. MiniCheck-DeBERTa-v3-Large outperform all exisiting specialized fact-checkers with a similar scale. See full results in our work.
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View details ↗MiniCheck-Flan-T5-Large
lytang📃 **MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents**,(EMNLP 2024, GitHub Repo) The model is based on Flan-T5-Large that predicts a binary label - 1 for supported and 0 for unsupported. The model is doing predictions on the *sentence-level*. It takes as input a document and a sentence and determine whether the sentence is supported by the document: **MiniCheck-Model(document, claim) -> {0, 1}** The performance of these models is evaluated on our new collected benchmark (unseen by our models during training), LLM-AggreFact, from 11 recent human annotated datasets on fact-checking and grounding LLM generations. MiniCheck-Flan-T5-Large outperform all exisiting specialized fact-checkers with a similar scale by a large margin (4-10% absolute increase) and is on par with GPT-4, but 400x cheaper. See full results in our work.
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View details ↗MiniCheck-RoBERTa-Large
lytangThe model is based on RoBERTA-Large that predicts a binary label - 1 for supported and 0 for unsupported. The model is doing predictions on the *sentence-level*. It takes as input a document and a sentence and determine whether the sentence is supported by the document: **MiniCheck-Model(document, claim) -> {0, 1}** MiniCheck-RoBERTa-Large is fine tuned from the trained RoBERTA-Large model from AlignScore (Zha et al., 2023) on 14K synthetic data generated from scratch in a structed way (more details in the paper). We also have other three MiniCheck model variants: - bespokelabs/Bespoke-Minicheck-7B (Model Size: 7B) - lytang/MiniCheck-Flan-T5-Large (Model Size: 0.8B) - lytang/MiniCheck-DeBERTa-v3-Large (Model Size: 0.4B)
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