サイト内検索

AI モデル 85003

• MiniMax-M3.1-Flash-NVFP4-GGUF↗

  AMAImedia

• TinyLlama-1.1B-Chat-v1.0-GGUF↗

  andrijdavid

• auto_e2e↗

  AutowareFoundation

• ReadyArt_Forgotten-Safeword-24B-3.6-GGUF↗

  bartowski

• Replete-LLM-Mist-Nemo-12b-test-merged-250k-GGUF↗

  bartowski

• GRASP↗

  caithmac

• Qwen3.5-122B-A10B-GM-E32-GGUF↗

  cgg507

• resnet18-eurosat↗

  cm93

• command-a-vision-07-2025↗

  Cohere

• bge-small-zh-v1.5-gguf↗

  CompendiumLabs

  Quantized and unquantized embedding models in GGUF format for use with `llama.cpp`. A large benefit over `transformers` is almost guaranteed and the benefit over ONNX will vary based on the application, but this seems to provide a large speedup on CPU and a modest speedup on GPU for larger models. Due to the relatively small size of these models, quantization will not provide huge benefits, but it does generate up to a 30% speedup on CPU with minimal loss in accuracy. Here `texts` can either be a string or a list of strings, and the return value is a list of embedding vectors. The inputs are grouped into batches automatically for efficient execution. There is also LangChain integration through `langchain_community.embeddings.LlamaCppEmbeddings`.