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EsoTest_GGUFs
Esobold- **GhostShip-31b-Q4_K_M.gguf** - FM nearswap merge: Gemma4-Gutenberg-31B-Heretic 0.0007 linear across layers (avg 4e-4). Same family as Robinson/BenGunn, ~4x peak strength.
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View details ↗kev-4b-demo-gguf
espetro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 ↗kev-9b-gguf
espetro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 ↗fastspeech2_conformer
espnet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 ↗hubert_dummy
espnet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 ↗owsm_ctc_v4_1B
espnetOpen Whisper-style Speech Model (OWSM) is the first **fully open** Whisper-style speech foundation model. It reproduces and advances OpenAI's Whisper-style training using publicly available data and open-source toolkits. The code, pre-trained model weights, and training logs are publicly released to promote open science in speech foundation models. OWSM-CTC (Peng et al., ACL 2024) is a novel encoder-only speech foundation model based on hierarchical multi-task self-conditioned CTC. It supports multilingual speech recognition, speech translation, and language identification within a single non-autoregressive model. OWSM-CTC v4 is trained for three epochs on 320k hours of public audio data covering multilingual speech recognition, any-to-any speech translation, and language identification. The newly curated data are publicly released: https://huggingface.co/datasets/espnet/yodas_owsmv4
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View details ↗powsm
espnet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 ↗powsm_ctc
espnet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 ↗voxcelebs12_rawnet3
espnet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 ↗eai-distill-0.5b
EssentialAIEAI-Distill-0.5b is a fine-tuned version of Qwen2.5-0.5B-Instruct designed for document classification across 12 taxonomic categories. This model is optimized for high-throughput classification of web documents and produces structured metadata for large-scale dataset curation. The model classifies documents across the following dimensions: - **📚 Free Decimal Correspondence (FDC)**: Subject matter classification based on the Dewey Decimal System - **🧠 Bloom's Taxonomy**: Cognitive process (Remember/Understand/Apply/Analyze/Evaluate/Create) and knowledge domain (Factual/Conceptual/Procedural/Metacognitive) - **📄 Document Type**: Web page categorization (News, Academic, Reference, Code, Social, etc.) - **🔍 Content Quality**: Extraction artifacts, missing content detection - **🎓 Educational Metadata**: Reasoning depth, technical correctness, educational level - **🤖 Base Model**: Qwen2.5-0.5B-Instruct - **📊 Training Data**: 82B synthetic tokens generated by Qwen2.5-32B-Instruct (teacher model) on 104M Common Crawl documents - **⚙️ Optimizer**: AdamW (β₁=0.9, β₂=0.95, weight_decay=0.1) - **📈 Learning Rate**: 1×10⁻⁴ with linear warmup (2B tokens), cosine decay to 1×10⁻⁵, then linear anneal to 0 - **📦 Batch Size**: 2M tokens - **📏 Sequence Length**: 16,384 tokens - **💻 Hardware**: Trained on AMD MI300x GPUs
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View details ↗rnj-1
EssentialAI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 ↗rnj-1.5-instruct
EssentialAI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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EssentialAI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 ↗NSFW
Estebandidoooi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 ↗qwen3.5-2b-physics
eswaroy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 ↗oasis-500m
Etched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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