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AI 모델 449

• chinese-hubert-large↗

  TencentGameMate

  Pretrained on 10k hours WenetSpeech L subset. More details in TencentGameMate/chinese_speech_pretrain This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data.

• OLMoE-1B-7B-0125-Instruct↗

  allenai

  OLMoE-1B-7B-0125-Instruct January 2025 is post-trained variant of the OLMoE-1B-7B January 2025 model, which has undergone supervised finetuning on an OLMo-specific variant of the Tülu 3 dataset and further DPO training on this dataset, and finally RLVR training using this data. Tülu 3 is designed for state-of-the-art performance on a diversity of tasks in addition to chat, such as MATH, GSM8K, and IFEval. Check out the OLMoE paper or Tülu 3 paper for more details! OLMo is a series of **O**pen **L**anguage **Mo**dels designed to enable the science of language models. These models are trained on the Dolma dataset. We are releasing all code, checkpoints, logs (coming soon), and associated training details. The core models released in this batch include the following: - **Model type:** A model trained on a mix of publicly available, synthetic and human-created datasets. - **Language(s) (NLP):** Primarily English - **License:** Apache 2.0 - **Finetuned from model:** allenai/OLMoE-1B-7B-0125-DPO

• Apertus-v1.1-0.5B↗

  swiss-ai

  Apertus-v1.1 is a series of highly efficient, 0.5-4B billion parameter language models designed to extend the fully-open and compliant Apertus ecosystem to highly constrained hardware environments. The models rely on a dense transformer architecture featuring grouped-query attention and xIELU activations. To achieve high performance with a minimized memory footprint, this model uses tied embeddings and a deeper, thinner architectural design. Instead of standard pre-training, Apertus-v1.1 models were created using pre-training distillation (PD) from the Apertus-8B-2509 teacher model. They were trained on 1.7T tokens from Phase 5 of the original Apertus data pipeline—the highest quality tier of filtered documents, code, and instruction samples without introducing any new data sources or licenses.

• Anima↗

  circlestone-labs

  Anima is a 2 billion parameter text-to-image model created via a collaboration between CircleStone Labs and Comfy Org. It is focused mainly on anime concepts, characters, and styles, but is also capable of generating a wide variety of other non-photorealistic content. The model is designed for making illustrations and artistic images, and will not work well at realism. It is trained on several million anime images and about 800k non-anime artistic images. No synthetic data was used for training. The knowledge cut-off date for the anime training data is September 2025. - Anima-Base - The pretrained, unrefined base model. Maximum flexibility, diversity, and style adherence. - LoRAs should be trained using this version. - Anima-Aesthetic - Fine-tuned for better consistency and a higher quality default art style. - v1.0b is an alternate version that is just an aesthetics full finetune, without additional style adjustment and stabilization loras merged in like 1.0. I personally think 1.0 is better. - Anima-Turbo - Distilled version for fast generations. - Use at CFG 1 and 8-12 steps. - The distillation process also increases stability and gives the model a strong default style, but reduces diversity.

• Wan2.1_T2V_14B_FusionX-GGUF↗

  QuantStack

  All quantized versions were created from the base T2V FP16 model using the conversion scripts provided by city96, available at the ComfyUI-GGUF GitHub repository. The model files can be used in ComfyUI with the ComfyUI-GGUF custom node. Place the required model(s) in the following folders:

• mbart-large-50↗

  facebook

  mBART-50 is a multilingual Sequence-to-Sequence model pre-trained using the "Multilingual Denoising Pretraining" objective. It was introduced in Multilingual Translation with Extensible Multilingual Pretraining and Finetuning paper. mBART-50 is a multilingual Sequence-to-Sequence model. It was introduced to show that multilingual translation models can be created through multilingual fine-tuning. Instead of fine-tuning on one direction, a pre-trained model is fine-tuned on many directions simultaneously. mBART-50 is created using the original mBART model and extended to add extra 25 languages to support multilingual machine translation models of 50 languages. The pre-training objective is explained below. **Multilingual Denoising Pretraining**: The model incorporates N languages by concatenating data: `D = {D1, ..., DN }` where each Di is a collection of monolingual documents in language `i`. The source documents are noised using two schemes, first randomly shuffling the original sentences' order, and second a novel in-filling scheme, where spans of text are replaced with a single mask token. The model is then tasked to reconstruct the original text. 35% of each instance's words are masked by random sampling a span length according to a Poisson distribution `(λ = 3.5)`. The decoder input is the original text with one position offset. A language id symbol `LID` is used as the initial token to predict the sentence.

• gemma-3-1b-pt↗

  unsloth

  Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. These models were trained on a dataset of text data that includes a wide variety of sources. The 27B model was trained with 14 trillion tokens, the 12B model was trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens and 1B with 2 trillion tokens. Here are the key components: The combination of these diverse data sources is crucial for training a powerful multimodal model that can handle a wide variety of different tasks and data formats.

• Solon-embeddings-large-0.1↗

  OrdalieTech

  **Instructions :** Add "query : " before the *query* to retrieve to increase performance of retrieval. No instructions needed for *passages*. These results have been obtained through 9 french benchmarks on a variety of text similarity tasks (classification, reranking, STS) : - AmazonReviewsClassification (MTEB) - MassiveIntentClassification (MTEB) - MassiveScenarioClassification (MTEB) - MTOPDomainClassification (MTEB) - MTOPIntentClassification (MTEB) - STS22 (MTEB) - MiraclFRRerank (Miracl) - OrdalieFRSTS (Ordalie) - OrdalieFRReranking (Ordalie) We created OrdalieFRSTS and OrdalieFRReranking to enhance the benchmarking capabilities of French STS and reranking assessments.

• granite-3.3-2b-base↗

  ibm-granite

  Granite-3.3-2B-Base is a decoder-only language model with a 128K token context window. It improves upon Granite-3.1-2B-Base by adding support for Fill-in-the-Middle (FIM) using specialized tokens, enabling the model to generate content conditioned on both prefix and suffix. This makes it well-suited for code completion tasks. **Supported Languages:** English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 3.3 models for languages beyond these 12 languages. **Intended Use:** Prominent use cases of LLMs in text-to-text generation include summarization, text classification, extraction, question-answering, and other long-context tasks. All Granite Base models are able to handle these tasks as they were trained on a large amount of data from various domains. Moreover, they can serve as baseline to create specialized models for specific application scenarios.

• kling-video-multi-elements-create↗

  快手 · 可灵

  콰이쇼 커링은 공식 인증 API를 제공하며, 안전하고 안정적입니다. 동기화 업데이트는 공식 모든 모델을 지원하며, 과금 방식도 공식과 동일합니다. 연동 시에는 공식 API 도메인과 토큰만 본사로 교체하면 됩니다.