Search FoxAI

AI Models 84759

• LoneStriker-DeepSeek-R1-Distill-Llama-70B-8.0bpw-h8-exl2-GGUF-smashed↗

  PrunaAI

• Asena_ESP32_MAX↗

  pthinc

• 0x01-8x7b-iMat-GGUF↗

  Quant-Cartel

• Josiefied-Qwen2-7B-Instruct-abliterated-GGUF↗

  QuantFactory

• Qwen3-1.7B-MLX-4bit↗

  阿里巴巴 · Qwen

• Qwen3-Embedding-0.6B-GGUF↗

  阿里巴巴 · Qwen

  The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B). This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining. **Exceptional Versatility**: The embedding model has achieved state-of-the-art performance across a wide range of downstream application evaluations. The 8B size embedding model ranks **No.1** in the MTEB multilingual leaderboard (as of June 5, 2025, score **70.58**), while the reranking model excels in various text retrieval scenarios. **Comprehensive Flexibility**: The Qwen3 Embedding series offers a full spectrum of sizes (from 0.6B to 8B) for both embedding and reranking models, catering to diverse use cases that prioritize efficiency and effectiveness. Developers can seamlessly combine these two modules. Additionally, the embedding model allows for flexible vector definitions across all dimensions, and both embedding and reranking models support user-defined instructions to enhance performance for specific tasks, languages, or scenarios.

• Mixtral-8x7B-Instruct-v0.1↗

  RedHatAI

  This model can be deployed efficiently on vLLM, Red Hat Enterprise Linux AI, and Openshift AI, as shown in the example below. > [!TIP] > PRs to correct the transformers tokenizer so that it gives 1-to-1 the same results as the mistral-common reference implementation are very welcome! --- The Mixtral-8x7B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts. The Mixtral-8x7B outperforms Llama 2 70B on most benchmarks we tested.

• 0-hero_-_Matter-0.2-7B-gguf↗

  RichardErkhov

  Matter-0.2-7B - GGUF - Model creator: https://huggingface.co/0-hero/ - Original model: https://huggingface.co/0-hero/Matter-0.2-7B/ Original model description: --- license: apache-2.0 datasets: - 0-hero/Matter-0.2 language: - en --- Matter 7B 0.2 is full-finetune on the **Matter 0.2 dataset** (not yet released), which is curated from over 35 datsets analyzing >6B tokens

• trellis-normal-v0-1↗

  Stable-X

• sp5↗

  tao1000