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AI 模型 84759

• Unholy-8B-DPO-OAS-GGUF↗

  mradermacher

• f5-tts-uzbek↗

  mrmuminov

• Qwen3.5-9B-GPTQ-INT8↗

  mssfj

• Ministral-3-14B-Reasoning-2512-GGUF↗

  Mungert

• OpenCodeReasoning-Nemotron-14B-GGUF↗

  Mungert

• qwen3-4b-transcoders↗

  mwhanna

• Huihui-Qwen3.5-9B-abliterated-AWQ-4bit↗

  nemozxy123

• pubmedbert-base-embeddings↗

  NeuML

  This is a PubMedBERT-base model fined-tuned using sentence-transformers. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. The training dataset was generated using a random sample of PubMed title-abstract pairs along with similar title pairs. PubMedBERT Embeddings produces higher quality embeddings than generalized models for medical literature. Further fine-tuning for a medical subdomain will result in even better performance. This model can be used to build embeddings databases with txtai for semantic search and/or as a knowledge source for retrieval augmented generation (RAG).

• nomic-embed-code↗

  nomic-ai

  `nomic-embed-code` is a state-of-the-art code embedding model that excels at code retrieval tasks: - **High Performance**: Outperforms Voyage Code 3 and OpenAI Embed 3 Large on CodeSearchNet - **Multilingual Code Support**: Trained for multiple programming languages (Python, Java, Ruby, PHP, JavaScript, Go) - **Advanced Architecture**: 7B parameter code embedding model - **Fully Open-Source**: Model weights, training data, and evaluation code released - **Total Parameters**: 7B - **Training Approach**: Trained on the CoRNStack dataset with dual-consistency filtering and progressive hard negative mining - **Supported Languages**: Python, Java, Ruby, PHP, JavaScript, and Go

• cats↗

  nroggendorff