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AI モデル 84759
- inclusionAI-ZwZ2-4B-GGUF↗
prithivMLmods
- Qwen3.5-2B-rk3588↗
Qengineering
- gemma-7b-aps-it-GGUF↗
QuantFactory
- CodeQwen1.5-7B↗
阿里巴巴 · Qwen
- Meta-Llama-3.1-8B-quantized.w8a8↗
RedHatAI
- JayHyeon_-_Qwen2.5-0.5B-SFT-2e-5-5ep-gguf↗
RichardErkhov
- japanese-gpt2-medium↗
rinna
This repository provides a medium-sized Japanese GPT-2 model. The model was trained using code from Github repository rinnakk/japanese-pretrained-models by rinna Co., Ltd. tokenizer = AutoTokenizer.from_pretrained("rinna/japanese-gpt2-medium", use_fast=False) tokenizer.do_lower_case = True # due to some bug of tokenizer config loading The model was trained on Japanese CC-100 and Japanese Wikipedia to optimize a traditional language modelling objective on 8\\*V100 GPUs for around 30 days. It reaches around 18 perplexity on a chosen validation set from the same data.
- dream_pop-acestep1.5-xl-v1↗
ryanontheinside
- blip-image-captioning-large↗
Salesforce
Model card for image captioning pretrained on COCO dataset - base architecture (with ViT large backbone). Authors from the paper write in the abstract: *Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones. We achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2.7% in average recall@1), image captioning (+2.8% in CIDEr), and VQA (+1.6% in VQA score). BLIP also demonstrates strong generalization ability when directly transferred to videolanguage tasks in a zero-shot manner. Code, models, and datasets are released.*
- moirai-1.0-R-small↗
Salesforce
Moirai, the Masked Encoder-based Universal Time Series Forecasting Transformer is a Large Time Series Model pre-trained on LOTSA data. For more details on the Moirai architecture, training, and results, please refer to the paper. Fig. 1: Overall architecture of Moirai. Visualized is a 3-variate time series, where variates 0 and 1 are target variables (i.e. to be forecasted, and variate 2 is a dynamic covariate (values in forecast horizon known). Based on a patch size of 64, each variate is patchified into 3 tokens. The patch embeddings along with sequence and variate id are fed into the Transformer. The shaded patches represent the forecast horizon to be forecasted, whose corresponding output representations are mapped into the mixture distribution parameters. This release is for research purposes only in support of an academic paper. Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes. We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model. We encourage users to consider the common limitations of AI, comply with applicable laws, and leverage best practices when selecting use cases, particularly for high-risk scenarios where errors or misuse could significantly impact people’s lives, rights, or safety. For further guidance on use cases, refer to our AUP and AI AUP.