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AI Models 84759
- dat-lequoc_-_fast-apply-16bit-v0.v15.4-Qwen2.5-Coder-1.5B-Instruct-gguf↗
RichardErkhov
- Srijith-rkr_-_deepseek_base_1e-4_NO_cot_only_failed_samples_3_epoch-gguf↗
RichardErkhov
- sot-phase1-wikitext103-dense-seed42-tc017↗
sbartlett97
- KR-ELECTRA-generator↗
snunlp
This is a release of a Korean-specific ELECTRA model with comparable or better performances developed by the Computational Linguistics Lab at Seoul National University. Our model shows remarkable performances on tasks related to informal texts such as review documents, while still showing comparable results on other kinds of tasks. We pre-trained our KR-ELECTRA model following a base-scale model of ELECTRA. We trained the model based on Tensorflow-v1 using a v3-8 TPU of Google Cloud Platform. We followed the training parameters of the base-scale model of ELECTRA.
- clip-vit-base-patch16_stl10↗
tanganke
- ai-girlfriend-natsume↗
TAOTAO777
All model files in this repository are **existing community open-source models**. I am only mirroring and redistributing them for convenience. **I claim no ownership** over any of these models. **This project is purely non-profit and personal.** No commercial use intended. If any original author objects to mirror distribution, please contact me to take it down.
- PhotoMaker-V2↗
TencentARC
Users can input one or a few face photos, along with a text prompt, to receive a customized photo or painting within seconds (no training required!). Additionally, this model can be adapted to any base model based on SDXL or used in conjunction with other LoRA modules. You can directly download the model in this repository. You also can download the model in python script: While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
- Airoboros-L2-70b-2.2.1-GGUF↗
TheBloke
- kokoro-tts-kazakh↗
TilLabs
- mobilenetv3_large_100.ra4_e3600_r224_in1k↗
timm
Trained with `timm` scripts using hyper-parameters inspired by the MobileNet-V4 small, mixed with go-to hparams from `timm` and "ResNet Strikes Back". A collection of hparams (timm .yaml config files) for this training series can be found here: https://gist.github.com/rwightman/f6705cb65c03daeebca8aa129b1b94ad