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AI 模型 84759

• opencoti-llamafile↗

  ManniX-ITA

  Self-contained single-file inference engine from the opencoti project — a Mozilla-Ocho llamafile base carrying the opencoti patch series: advanced KV residency/quantization (PolyKV, KVarN — see `docs/features/kvarn.md`), the KV rolling window (a KV cache larger than VRAM), elastic multi-session serving (elastic slots, an enforced KV admission gate with fast 429 + `Retry-After`, PolyKV shared-prefix pools with a REST control plane), DCA long-context extension, MTP speculative decode, sparse attention, RYS layer duplication, CUDA **and Vulkan** GPU backends, and a runtime introspection/control API. **Start with USAGE.md** — it explains exactly how this engine diverges from upstream llamafile, every added feature, its flags, defaults, limitations, and which features compose. This repo hosts the packaged release artifacts (they exceed GitHub's 2 GiB release-asset cap) plus the full patch series under `patches/` and the project documentation under `docs/`. Release notes and per-release `SHA256SUMS` live under `releases/` and are mirrored on the corresponding GitHub release at mann1x/opencoti.

• LCO-Embedding-Omni-7B-GGUF↗

  marksverdhei

• harrier-oss-v1-27b↗

  Microsoft

  harrier-oss-v1 is a family of multilingual text embedding models developed by Microsoft. The models use decoder-only architectures with last-token pooling and L2 normalization to produce dense text embeddings. They can be applied to a wide range of tasks, including but not limited to **retrieval**, **clustering**, **semantic similarity**, **classification**, **bitext mining**, and **reranking**. The models achieve state-of-the-art results on the Multilingual MTEB v2 benchmark as of the release date. All models are trained with contrastive learning objectives on a large-scale mixture of multilingual datasets covering diverse tasks. The 270m and 0.6b variants are additionally trained with knowledge distillation from larger embedding models. Have a look at config_sentence_transformers.json for the prompts that are pre-configured, such as `web_search_query`, `sts_query`, and `bitext_query`. You can also use a custom instruction directly via e.g. `model.encode(queries, prompt="Instruct: Retrieve semantically similar text\nQuery: ")`.

• Phi-4-mini-reasoning-onnx↗

  Microsoft

• STELLAR↗

  Microsoft

• HyperCLOVAX-SEED-Text-Instruct-1.5B-Batch16↗

  mobilint

• mol-base-distilled-text-checkpoints↗

  mohammadmahdinouri

• GUI-Owl-1.5-8B-Instruct↗

  mPLUG

  GUI-Owl 1.5 is the next-generation native GUI agent model family built on Qwen3-VL. It supports multi-platform GUI automation across desktops, mobile devices, browsers, and more. Powered by a scalable hybrid data flywheel, unified agent capability enhancement, and multi-platform environment RL (MRPO), GUI-Owl 1.5 offers a full spectrum of models. **Key highlights:** - 🏆 **State-of-the-art** among multi-platform GUI models on OSWorld-Verified, AndroidWorld, Mobile-World, WindowsAA, ScreenSpot-v2, ScreenSpot-Pro, and more. - 🔧 **Tool & MCP calling**: Native support for external tool invocation and MCP server coordination, achieving top performance on OSWorld-MCP and Mobile-World. - 🧠 **Long-horizon memory**: Built-in memory capability without external workflow orchestration, leading all native agent models on MemGUI-Bench. - 🤝 **Multi-agent ready**: Serves both as a standalone end-to-end agent and as specialized roles (planner, executor, verifier, notetaker) within the Mobile-Agent-v3.5 framework. - ⚡ **Instruct & Thinking variants**: Smaller instruct models for fast inference and edge deployment; larger thinking models for complex tasks requiring planning and reflection. Please refer to the technical report for detailed results on ScreenSpot-v2, ScreenSpot-Pro, OSWorld-G, MMBench-GUI, and more.

• Abomimegadarkgasinalgottenevereamession-i1-GGUF↗

  mradermacher

• AgentMercury-Qwen3.5-4B-i1-GGUF↗

  mradermacher