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AI Models 84759
- Triumvirate-Qwopus-MiMo-Ornith-9B-Coder↗
alst10
- AquilaMoE↗
BAAI
- Tiger-Gemma-9B-v1-GGUF↗
bartowski
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
- vesselFM↗
bwittmann
- animagine-xl-3.0↗
cagliostrolab
**Animagine XL 3.0** is the latest version of the sophisticated open-source anime text-to-image model, building upon the capabilities of its predecessor, Animagine XL 2.0. Developed based on Stable Diffusion XL, this iteration boasts superior image generation with notable improvements in hand anatomy, efficient tag ordering, and enhanced knowledge about anime concepts. Unlike the previous iteration, we focused on training the model to learn concepts rather than just aesthetics. - **Developed by**: Cagliostro Research Lab - **Model type**: Diffusion-based text-to-image generative model - **Model Description**: Animagine XL 3.0 is engineered to generate high-quality anime images from textual prompts. It features enhanced hand anatomy, better concept understanding, and prompt interpretation, making it the most advanced model in its series. - **License**: CreativeML Open RAIL++-M License - **Finetuned from model**: Animagine XL 2.0 Animagine XL 3.0 is accessible through user-friendly platforms such as Gradio and Google Colab:
- DeepSeek-Coder-V2-Lite-Instruct↗
DeepSeek
We present DeepSeek-Coder-V2, an open-source Mixture-of-Experts (MoE) code language model that achieves performance comparable to GPT4-Turbo in code-specific tasks. Specifically, DeepSeek-Coder-V2 is further pre-trained from an intermediate checkpoint of DeepSeek-V2 with additional 6 trillion tokens. Through this continued pre-training, DeepSeek-Coder-V2 substantially enhances the coding and mathematical reasoning capabilities of DeepSeek-V2, while maintaining comparable performance in general language tasks. Compared to DeepSeek-Coder-33B, DeepSeek-Coder-V2 demonstrates significant advancements in various aspects of code-related tasks, as well as reasoning and general capabilities. Additionally, DeepSeek-Coder-V2 expands its support for programming languages from 86 to 338, while extending the context length from 16K to 128K. In standard benchmark evaluations, DeepSeek-Coder-V2 achieves superior performance compared to closed-source models such as GPT4-Turbo, Claude 3 Opus, and Gemini 1.5 Pro in coding and math benchmarks. The list of supported programming languages can be found here. We release the DeepSeek-Coder-V2 with 16B and 236B parameters based on the DeepSeekMoE framework, which has actived parameters of only 2.4B and 21B , including base and instruct models, to the public.
- FLUX.2-dev-bnb-4bit↗
diffusers
This repository contains the NF4 quantized DiT and the text encoders from https://huggingface.co/black-forest-labs/FLUX.2-dev. The VAE is not quantized.
- yolov5-det↗
EdgeFirst
- SANA1.5_4.8B_1024px↗
Efficient-Large-Model
- pythia-14m-deduped↗
EleutherAI
> **Note:** Pythia-14M-deduped was trained after the original Pythia suite at the request of interpretability researchers who wanted a smaller model with the same training setup. It uses the same data, tokenizer, hyperparameter conventions, and checkpoint schedule as the rest of the Pythia suite. Unlike the original Pythia models (70M--12B), Pythia-14M-deduped was only trained on the deduplicated Pile and does not have a separate non-deduplicated variant. The primary intended use of Pythia is research on the behavior, functionality, and limitations of large language models. This suite is intended to provide a controlled setting for performing scientific experiments. We provide 154 checkpoints per model: - Initial `step0` - 10 log-spaced checkpoints `step{1,2,4...512}` - 143 evenly-spaced checkpoints from `step1000` to `step143000` These checkpoints are hosted on Hugging Face as branches. Note that branch `143000` corresponds exactly to the model checkpoint on the `main` branch of each model.