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AI 模型 84759
- dolphin-2.9-llama3-70b-GGUF-smashed↗
PrunaAI
- DeepSeek-R1-Distill-Qwen-1.5B-GGUF↗
QuantFactory
- DuckDB-NSQL-7B-v0.1-GGUF↗
QuantFactory
- Qwen3 30B A3B (Reasoning)↗
阿里巴巴 · Qwen
- Qwen3-Embedding-4B-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.
- Qwen3-VL-4B-Thinking-GGUF↗
redponike
- rhysjones_-_gpt2-124M-edu-fineweb-10B-gguf↗
RichardErkhov
- saboof_-_codegemma-2b-merged-docstring-gguf↗
RichardErkhov
- We-Want-GPU_-_SOLAR-10.7B-orca-alpaca-gpt4-lora-653-gguf↗
RichardErkhov
- HRM-Text-1B↗
sapientinc
A 1 B-parameter language model checkpoint built on the **Hierarchical Reasoning Model (HRM)** architecture, trained by Sapient Intelligence from scratch on structured public datasets. HRM is a dual-timescale recurrent architecture: two Transformer modules (H = high-level / slow, L = low-level / fast) iterate over the same input embeddings for `H_cycles × (L_cycles + 1)` steps, with additive state injection (`z_L + z_H`). This gives effectively unbounded compute depth at bounded parameter count. This is a **pre-alignment** model checkpoint, not a chat or instruction-following assistant. It is pre-trained on a PrefixLM objective with condition prefix tokens and has **not** been multi-turn dialogue tuned, long-context adapted, instruction-tuned, RLHF-trained, or otherwise aligned for assistant-style use. If you want to use HRM-Text like a chat model, you would need to perform further alignment, such as SFT and/or RL, on task-specific data. This checkpoint is meant to serve as a starting point, not a finished assistant.