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AI 模型 84759

• ConceptCLIP↗

  JerrryNie

• ettin-decoder-150m↗

  jhu-clsp

• tofu_Llama-3.1-8B-Instruct_forget01_WGA↗

  JoaoBoer

• finbert-onnx↗

  jonngan

• 2-bit-LLMs↗

  KnutJaegersberg

  This is a model collection of mostly larger LLMs quantized to 2 bit with the novel quip# inspired approach in llama.cpp Sometimes both xs and xxs are available. Note that for some larger models, like Qwen-72b based models, the context length might be too large for most GPUs, so you have to reduce it yourself in textgen-webui via the n_ctx setting. Rope scaling for scaled models like longalpaca or yarn should be 8, set compress_pos_emb accordingly.

• kas-4b↗

  kpiya

• bilingual-embedding-large↗

  Lajavaness

  Bilingual-embedding is the Embedding Model for bilingual language: french and english. This model is a specialized sentence-embedding trained specifically for the bilingual language, leveraging the robust capabilities of XLM-RoBERTa, a pre-trained language model based on the XLM-RoBERTa architecture. The model utilizes xlm-roberta to encode english-french sentences into a 1024-dimensional vector space, facilitating a wide range of applications from semantic search to text clustering. The embeddings capture the nuanced meanings of english-french sentences, reflecting both the lexical and contextual layers of the language. @article{conneau2019unsupervised, title={Unsupervised cross-lingual representation learning at scale}, author={Conneau, Alexis and Khandelwal, Kartikay and Goyal, Naman and Chaudhary, Vishrav and Wenzek, Guillaume and Guzm{\'a}n, Francisco and Grave, Edouard and Ott, Myle and Zettlemoyer, Luke and Stoyanov, Veselin}, journal={arXiv preprint arXiv:1911.02116}, year={2019} } @article{reimers2019sentence, title={Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks}, author={Nils Reimers, Iryna Gurevych}, journal={https://arxiv.org/abs/1908.10084}, year={2019} }

• EXAONE-Deep-7.8B↗

  LGAI-EXAONE

  We introduce EXAONE Deep, which exhibits superior capabilities in various reasoning tasks including math and coding benchmarks, ranging from 2.4B to 32B parameters developed and released by LG AI Research. Evaluation results show that 1) EXAONE Deep **2.4B** outperforms other models of comparable size, 2) EXAONE Deep **7.8B** outperforms not only open-weight models of comparable scale but also a proprietary reasoning model OpenAI o1-mini, and 3) EXAONE Deep **32B** demonstrates competitive performance against leading open-weight models. > ### Note > The EXAONE Deep models are trained with an optimized configuration, > so we recommend following the Usage Guideline section to achieve optimal performance. The following table shows the evaluation results of reasoning tasks such as math and coding. The full evaluation results can be found in the documentation.

• LFM2-24B-A2B-MLX-4bit↗

  LiquidAI

• llm-jp-3-1.8b↗

  llm-jp

  This repository provides large language models developed by the Research and Development Center for Large Language Models at the National Institute of Informatics. The tokenizer of this model is based on huggingface/tokenizers Unigram byte-fallback model. The vocabulary entries were converted from `llm-jp-tokenizer v3.0`. Please refer to README.md of `llm-jp-tokenizer` for details on the vocabulary construction procedure (the pure SentencePiece training does not reproduce our vocabulary). The models released here are in the early stages of our research and development and have not been tuned to ensure outputs align with human intent and safety considerations.