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AI 模型 2119
- PP-OCRv6_small_rec_onnx↗
PaddlePaddle
PP-OCRv6: From 1.5M to 34.5M Parameters, Surpassing Billion-Scale VLMs on OCR Tasks **🔥 Official Website** **📝 Technical Report** PP-OCRv6 is a lightweight OCR system that combines architectural innovation with data-centric optimization. It redesigns the backbone, detection neck, and recognition neck around a unified MetaFormer-style building block with structural reparameterization. Three model tiers (medium, small, tiny) share the same block primitives, covering deployment scenarios from server to edge.
- DeepSeek-V4-Flash-0731-GGUF↗
unsloth
Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants. To run DeepSeek-V4-Flash-0731 in full precision lossless, run Q8 (UD-Q8_K_XL), which is 162GB and only 7GB bigger than Q4 (UD-Q4_K_XL). See our DeepSeek-V4 guide for quantization analysis and instructions. You can now run DeepSeek-V4-Flash-0731 in Unsloth Studio with toggles for High and Max thinking. New DSpark support allowing up to 2x faster decoding! Docs for DSpark **DeepSeek-V4-Flash-0731** is the official release of **DeepSeek-V4-Flash**, superseding the preview version, with substantially enhanced agentic capabilities. It has the same model structure as DeepSeek-V4-Flash-DSpark, i.e. it comes with a speculative decoding module attached.
- Wan2.2-TI2V-5B↗
Wan-AI
💜 Wan    ｜    🖥️ GitHub    |   🤗 Hugging Face    |   🤖 ModelScope    |    📑 Technical Report    |    📑 Blog    |   💬 WeChat Group    |    📖 Discord    We are excited to introduce **Wan2.2**, a major upgrade to our foundational video models. With **Wan2.2**, we have focused on incorporating the following innovations: - 👍 **Effective MoE Architecture**: Wan2.2 introduces a Mixture-of-Experts (MoE) architecture into video diffusion models. By separating the denoising process cross timesteps with specialized powerful expert models, this enlarges the overall model capacity while maintaining the same computational cost.
- IndicBERTv2-MLM-Sam-TLM↗
ai4bharat
- Qari-OCR-0.4.0-VL-4B-Instruct-GGUF↗
marwan-osama
- Andy-4.2-GGUF↗
Mindcraft-CE
The Mindcraft CE team introduces **Andy-4.2**, noted as **the best** local AI you can use to play Minecraft with. Thinking faster than Andy-4.1, being able to carry out more actions than Andy-4, and rivaling models **10x it's size.** Andy-4.2 uses largely the same formula as Andy-4.1, but introduces a **new architecture** from the Qwen3.5 series which makes the model not only smarter, but more efficient. Using **Gated Deltanet** attention allows Andy-4.2 to run on a **single** RTX 3090, with 256k tokens of context, at a staggering 8-bit quantization. Andy-4.2 is also the **first local model** capable of getting a full set of diamond armour, with **zero human interaction**
- PP-OCRv6_tiny_det_onnx↗
PaddlePaddle
PP-OCRv6: From 1.5M to 34.5M Parameters, Surpassing Billion-Scale VLMs on OCR Tasks **🔥 Official Website** **📝 Technical Report** PP-OCRv6 is a lightweight OCR system that combines architectural innovation with data-centric optimization. It redesigns the backbone, detection neck, and recognition neck around a unified MetaFormer-style building block with structural reparameterization. Three model tiers (medium, small, tiny) share the same block primitives, covering deployment scenarios from server to edge.
- OLMo-2-1124-13B-Instruct↗
allenai
Upon the initial release of OLMo-2 models, we realized the post-trained models did not share the pre-tokenization logic that the base models use. As a result, we have trained new post-trained models. The new models are available under the same names as the original models, but we have made the old models available with a postfix "-preview". See OLMo 2 Preview Post-trained Models for the colleciton of the legacy models. OLMo 2 13B Instruct November 2024 is post-trained variant of the OLMo-2 13B November 2024 model, which has undergone supervised finetuning on an OLMo-specific variant of the Tülu 3 dataset, and finally RLVR training using this data. Tülu 3 is designed for state-of-the-art performance on a diversity of tasks in addition to chat, such as MATH, GSM8K, and IFEval. Check out the OLMo 2 paper or Tülu 3 paper for more details! OLMo is a series of **O**pen **L**anguage **Mo**dels designed to enable the science of language models. These models are trained on the Dolma dataset. We are releasing all code, checkpoints, logs (coming soon), and associated training details. The core models released in this batch include the following:
- coreml-sam2.1-small↗
apple
- DeepSeek-V4.1-Flash-1017-UNCENSORED-FP8↗
dealignai
> [!NOTE] > **Please credit if you build on this.** If you redistribute, quantize, fine-tune, serve, benchmark, or write about this model, a line like > _"Based on `dealignai/DeepSeek-V4.1-Flash-1017-UNCENSORED-FP8`"_ > in your model card or write-up means a lot. Thank you. > [!TIP] > **Quantizing this FP8 checkpoint directly won't give the best quality.** Low-bit quants that are naively derived from these weights carry quantization error on top of the abliteration edit, and compliance can regress at low bits. For a proper low-bit release, redo the abliteration pipeline on the target precision rather than quantizing this bundle after the fact. (The sibling EXL3 2.9 bpw UNCENSORED is built that way end-to-end.) > [!IMPORTANT] > Native FP8 (e4m3) with ue8m0 block scales + FP4 MoE experts. 1M-token context. Multimodal > (image + video) + tool calling + reasoning-effort tiers (`low` / `high` / `max`) all preserved. > **Recommended sampling:** `temperature=1.0`, `top_p=1.0`, `max_tokens ≥ 256K` (heavy reasoner).