lumina-next-135m
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View details ↗DISCOVER & COMPARE
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tinycomma-1.8b-llama3-tokenizer
jacquelinehe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 ↗t5gemma-2-4b-encoder
jadanovitch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 ↗mar
jadechoghari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 ↗marlin-2b-gguf
jadeonrails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 ↗voice-gender-classifier
JaesungHuh- This repo contains the inference code to use pretrained human voice gender classifier. - You could also try 🤗Huggingface online demo. First, clone the original github repository For those who need pretrained weights, please download it in here
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View details ↗Qwen3-ASR-GGUF
Jaffe2718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 ↗beru_custom
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View details ↗Focus
Jaidchen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 ↗fable-coder-35B-A3B
Jailbroken1150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 ↗gemma-4-12b-constraintkit-GGUF
jajmangold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 ↗LFM-2.5-8B-1B-hermes-ft
jakeatx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 ↗A speed-oriented quantization of Qwen3.8-27B for a single RTX 3090 / 3090 Ti (24 GB), designed to fit a populated 200K-token context with an 8-bit key cache and the model's own MTP speculative head, and to decode faster per speculative round than both Unsloth's Q3_K_XL and Q4_K_M on that card. **On the name.** ATX-IQ4_XS-M reads as: base format IQ4_XS on the bulk tensors, upgrade pattern M. In llama.cpp's vocabulary the XS in IQ4_XS is part of the format's name (the 4.25 bits-per-weight super-block layout of the IQ4 codebook), not a mix size, while the S/M/L suffix on the K-quants describes how many tensors are lifted above the base format. This file lifts the same tensors Q4_K_M does (Q5_0, Q6_K and Q8_0 on attention K/V, attention output, GDN output and FFN down), so it is an M-pattern mix on an IQ4_XS base: 4.56 bits per weight, between Q4_K_S (4.58) and Q4_K_M (4.82) in size. It was developed under the working name ATX-4-XS, which the GGUF filename, the tensor map and the run logs keep. The same recipe applied to Jackrong's Qwopus3.8-27B-Flash fine-tune, with a model-specific importance matrix and a paired speed comparison against its Q4_K_S, is at jakeatx/Qwopus3.8-27B-Flash-ATX-IQ4_XS-M-GGUF.
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View details ↗Qwen3.8-Perian-GGUF
jakeatx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 ↗wav2vec-vm-finetune
jakeBlandThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m for **voicemail detection**. It is trained on a dataset of call recordings to distinguish between **voicemail greetings** and **live human responses**. This model builds on **wav2vec2-xls-r-300m**, a self-supervised speech model trained on large-scale multilingual data. We fine-tuned it on the first two seconds of a call. - Automated voicemail detection in AI-powered call assistants. - Filtering voicemail responses in customer service and sales call automation.
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