BERT-Emotions-Classifier
ayoubkirouane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 ↗DISCOVER & COMPARE
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JapaneseTinyAgentLM-Action-3M
ayousanz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 ↗piper-plus-css10-ja-6lang
ayousanz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 ↗piper-plus-tsukuyomi-chan
ayousanzThe zs-v1 release is the existing 500 epoch Tsukuyomi-chan fine-tune from the same v7 epoch32 generation as the public base. Use its ONNX and config together. This fixed voice requires no reference WAV or speaker encoder. The release includes a fine-tuning checkpoint, exact hashes, synthetic samples, credits and observed training provenance. It is an experimental baseline. Listening review status: **recorded**. Fresh three-text CAM++ mean SECS was 0.6617 (FT), 0.6538 (legacy) and 0.6956 (public-base Zero-Shot); these results do not establish improved voice quality. See the release card and verification record for the review scope and limits. The original root model files, configuration, samples and description below remain available for existing consumers and package aliases. Downloading through an older package alias still selects those original files.
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View details ↗yomi-distill-ja
ayousanz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 ↗> **Update — September 27, 2026: IQ3_S-MTP is now available!** > > Added Qwen3.8-27B-Abliterated-GSQ-Orca-IQ3_S-MTP.gguf — **12.12 GB / 11.29 GiB**, built with the same selective GSQ–Orca method. Feedback and issue reports are welcome! > > **The detailed results below refer to the original IQ2_S release.** A compact, experimental GGUF with MTP included, built for local inference on smaller GPUs. It combines GSQ's quantized weights with selected abliterated weights from OrcaRouter and was developed and tested on an **RTX 3060 12 GB**. This is my first open-source model release. My goal is to share something useful for local use, with a straightforward account of how it was built and tested. Abliteration—the weight modification used to reduce refusal behavior—comes from OrcaRouter.
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View details ↗Qwen3.8-27B-GGUF
ayqzs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 ↗Qwen3.8-27B-Heretic-Abliterated-Uncensored-GGUF-Config
ayushcluster00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 ↗safetyvision-yolov8
ayushgupta7777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 ↗sexyGPT-Uncensored-Q4_0-GGUF
Ayyystin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 ↗AZ
Az2671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 ↗Qwen3.8-Flash-Next with **5 routed experts per token instead of 10**, healed so it stays close to the original, quantized to int4. It runs on **one DGX Spark (GB10, 128 GB)** at roughly 70-76 tokens/s. Everything needed to serve it is in this one repository, including the 49 GB FP8 n-gram table under `ple-table/`. Nothing else to download. That builds the serving image, downloads this repository, and starts an OpenAI-compatible server on port 8000. The scripts and the full explanation are in that repo.
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View details ↗lfm2.5-2.6b-fable5-coding-agent-heretic
AzariazIZI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 ↗Qwen3.8-27B-FP8-dynamic
AzatAI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 ↗prediksi-emosi-indobert
azizp128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 ↗Qwen-Image-2.1-GGUF
azukivc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 ↗Qwen3.8-27B-Uncensored-GGUF
azukivcUncensored Qwen3.8-27B, published as GGUF quantizations with the multi-token prediction (MTP) head retained and verified. Refusal behaviour has been **substantially reduced**, not eliminated. See Measured behaviour for the numbers. Capabilities, training data, and architecture are otherwise unchanged. > **MTP tensors verified, not assumed.** Abliteration drops the `mtp.*` tensors: the model > is re-saved through transformers, which does not carry the MTP head, while `config.json` > still advertises it. They are grafted back from the base checkpoint and every file is > inspected after quantization — see Method and Verification.
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View details ↗blossom-v5.1-34b
Azure99Blossom is a conversational large language model, fine-tuned on the Blossom Orca/Wizard/Chat/Math mixed dataset based on the Yi-1.5-34B pre-trained model. Blossom possesses robust general capabilities and context comprehension. Additionally, the high-quality Chinese and English datasets used for training have been made open source. Training was conducted in two stages. The first stage used 40K Wizard, 40K Orca, 10K Math single-turn instruction datasets, training for 1 epoch; the second stage used 10K Blossom chat multi-turn dialogue dataset, and 10% randomly sampled data from the first stage, training for 3 epochs.
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