videollm-online-8b-v1plus
chenjoya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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Chenkin-UniControl-XL
ChenkinLab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 ↗BSRoformer-GGUF
chenmozhijinOfficial GGUF model repository for the BSRoformer.cpp project. This repository contains BS Roformer/Mel-Band-Roformer models converted to the GGUF format, enabling high-performance, cross-platform local inference using the MelBandRoformer.cpp inference engine. This repository contains GGUF quantized versions of the following original models: | Original Model | Original Author | Recommended (Q8_0) | All Versions | | :--- | :--- | :--- | :--- | | mel-band-roformer-deux | becruily | Download | View Models | | BS-RoFormer | anvuew | Download | View Models | | MelBandRoformers (voc_fv6) | GaboxR67 | Download | View Models | To meet different hardware requirements, various quantized versions are provided: - **q8_0** (Recommended): 8-bit quantization. Significantly reduces memory usage and bandwidth requirements while maintaining audio quality almost identical to FP32. - **fp16**: 16-bit floating point. Suitable for scenarios requiring maximum precision. - **q4_0 / q4_1 / q5_0 / q5_1**: Lower-bit quantization, suitable for devices with low VRAM/RAM. *(Note: Norm and Bias weights remain in FP32 to ensure numerical stability)* Please use with the BSRoformer.cpp command-line tool. Download the executable for your system from BSRoformer.cpp (or compile it yourself). Download the desired `.gguf` file (e.g., `voc_fv6-Q8_0.gguf`) using the direct links in the **Model List** above, or browse all models from the `Files and versions` page of this repository. ```bash ./bs_roformer-cli model_q8_0.gguf input.wav output.wav ./bs_roformer-cli model_q8_0.gguf input.wav output.wav --overlap 2
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View details ↗UD-VLA_CALVIN_ABCD_D
chenpyyy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 ↗UD-VLA_LIBERO
chenpyyy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 ↗DeepEyes-7B
ChenShawn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 ↗propositionizer-wiki-flan-t5-large
chentong00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 ↗Taiwan-inquiry_7B_v2.1.gguf
ChenWeiLi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 ↗moziAI-27B-MTP
chenyumo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 ↗moziAI-35B-A3B-MOE-MTP
chenyumoEnglish | 简体中文 | 繁體中文 | 日本語 | 한국어 | हिन्दी | Deutsch | Français | Nederlands | Italiano | Русский | Español | Português | العربية | Bahasa Indonesia | Türkçe | Tiếng Việt | Polski MoziAI-35B-V3.8 is a locally deployable open-source multimodal AI large model developed by the team of Chen Yumo, a leading Chinese finance influencer. Built on the open-source base **Ornith-1.5-35B-A3B** (Qwen3.5-35B-A3B / Qwen3.6-35B-A3B architecture, MoE 35B, MIT + Apache-2.0 (Qwen3.5/3.6 & Gemma 4 components) license), it integrates the team's self-developed financial data + financial domain capabilities + dynamic seven-dimensional thinking framework + agent LOOP reflection and iteration mechanism + Uncensored characteristic + MoziSmartBit hybrid quantization algorithm. **💡 Size Advantage: only 15.9 GB** — the 35B-parameter MoE model is compressed to just **15.9 GB** via the self-developed MoziSmartBit quantization (about 30% smaller than standard Q4_K_M ~22GB). It fits in a single installer, runs on ordinary consumer GPUs (20GB VRAM+), reduces cloud token costs to **zero**, enables 7×24 hour token freedom, and ensures local data privacy and security. Licensed for **free commercial use** — zero barrier for individuals and enterprises.
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View details ↗Jev-alpha-26B-A4B
chenz53A standalone decision model derived from Google Gemma 4 26B A4B IT. It reads a state and typed questions, then exposes probabilities over the allowed options. This repository contains full merged BF16 weights. It does not require a LoRA adapter at inference. The model retains the upstream Gemma4ForConditionalGeneration architecture and tokenizer. Only text decision behavior is fine-tuned and evaluated; other modalities and general chat are not validated here. This is seed 42, KL weight 0.1, checkpoint 2100. It is not an average of three adapters. The development screen selects the KL weight. The final test does not select this seed. Training uses 8,400 questions, two epochs, LoRA rank 8, alpha 16, and dropout zero. The loss is option-token cross-entropy plus 0.1 times conditional reference-to-model KL. The reference top 64 tokens exclude the question's valid option tokens at the answer position. Base revision: `4d7ae4984b7db7de8f8457170b3f1a419ee76d52`. The 26B/A4B name follows the upstream size class; all base weights remain required in memory. These measurements reload the saved merged model with Transformers, without PEFT or an adapter. Equal-type final accuracy: **79.50%** on 1,100 questions. Each source contributes 100 final questions. The aggregate gives equal weight to noul, choice, and score. The merged model has its own fitted temperature: **1.736507**, from 300 separate calibration questions. BF16 merging changes numerical behavior. Use these release results instead of the earlier adapter or three-seed mean. Calibration error uses ten equal-width confidence bins. Lower Brier and calibration error are better.
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View details ↗cheon-cypher-0.8b-v1
cheonai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 ↗MomaGraph-R1
cheryyunl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 ↗Chesi-AI-stage1
Chesi-AI-Models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 ↗dm_qwen4b_emulator
chewwt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 ↗dm_qwen4b_noise_emulator
chewwt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 ↗hpo_qwen4b_emulator
chewwt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 ↗hpo_qwen8b_emulator
chewwt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 ↗krea2_nsfw
chfm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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