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AI 模型 84760
- gpt-5.6-sol↗
oroboros-labs
- 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.*