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AI モデル 2119

• BioLORD-2023-M↗

  FremyCompany

  This model was trained using BioLORD, a new pre-training strategy for producing meaningful representations for clinical sentences and biomedical concepts. State-of-the-art methodologies operate by maximizing the similarity in representation of names referring to the same concept, and preventing collapse through contrastive learning. However, because biomedical names are not always self-explanatory, it sometimes results in non-semantic representations. BioLORD overcomes this issue by grounding its concept representations using definitions, as well as short descriptions derived from a multi-relational knowledge graph consisting of biomedical ontologies. Thanks to this grounding, our model produces more semantic concept representations that match more closely the hierarchical structure of ontologies. BioLORD-2023 establishes a new state of the art for text similarity on both clinical sentences (MedSTS) and biomedical concepts (EHR-Rel-B).

• t5-efficient-tiny↗

  Google

  T5-Efficient-TINY is a variation of Google's original T5 following the T5 model architecture. It is a *pretrained-only* checkpoint and was released with the paper **Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers** by *Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler*. In a nutshell, the paper indicates that a **Deep-Narrow** model architecture is favorable for **downstream** performance compared to other model architectures of similar parameter count. > We generally recommend a DeepNarrow strategy where the model’s depth is preferentially increased > before considering any other forms of uniform scaling across other dimensions. This is largely due to > how much depth influences the Pareto-frontier as shown in earlier sections of the paper. Specifically, a > tall small (deep and narrow) model is generally more efficient compared to the base model. Likewise, > a tall base model might also generally more efficient compared to a large model. We generally find > that, regardless of size, even if absolute performance might increase as we continue to stack layers, > the relative gain of Pareto-efficiency diminishes as we increase the layers, converging at 32 to 36 > layers. Finally, we note that our notion of efficiency here relates to any one compute dimension, i.e., > params, FLOPs or throughput (speed). We report all three key efficiency metrics (number of params, > FLOPS and speed) and leave this decision to the practitioner to decide which compute dimension to > consider.

• MiniCPM-V-4.7-35B-A3B-FP8↗

  openbmb

  > **This repository hosts the FP8 (compressed-tensors, llm-compressor) quantized version of MiniCPM-V 4.7 35B-A3B.** For the original BF16 weights and the full model card, please refer to openbmb/MiniCPM-V-4.7-35B-A3B. **MiniCPM-V 4.7** is the latest model in the MiniCPM-V series, available in a **35B-A3B** variant, with **35B-A3B being the first MoE model in the MiniCPM-V series**. It builds on the family's single-image, multi-image, and video understanding capabilities, combining stronger foundation capabilities with efficient visual encoding and on-device inference. Notable features of MiniCPM-V 4.7 include: - 📱 **The First Open-Source MoE Model with Lossless, Smooth On-Device Inference.** With our **Lookahead Expert Scheduling (LES)** technique introduced alongside MiniCPM-V 4.7, combined with fine-grained layer-wise quantization and DSpark speculative sampling integration, MiniCPM-V 4.7 35B-A3B becomes the **first open-source MoE model to run smoothly on-device without compromising performance**, achieving a decoding speed of **13+ tokens/s with just 3 GB of memory on iPhone 17 Pro Max**. - 🔥 **A New Pareto Frontier in Performance and Efficiency.** As model capabilities continue to advance, inference efficiency has become an important measure of a model's value. On resource-constrained devices, delivering the strongest possible capabilities within a limited resource budget calls for higher capability density. The **35B-A3B** variant of MiniCPM-V 4.7 advances the performance–efficiency Pareto frontier for MLLMs. In Instruct mode, 35B-A3B **achieves a higher overall score in less inference time than Qwen3.6 35B-A3B**. - 🚀 **Efficient Architecture.** Based on the intra-ViT early compression technique from LLaVA-UHD v4, MiniCPM-V 4.7 compresses visual tokens early within the vision encoder, reducing computation in subsequent encoder layers. It also supports **mixed 4x/16x visual token compression**, allowing developers to balance visual detail and inference cost across tasks. - 💻 **Broad Platform and Chip Support.** MiniCPM-V 4.7 supports **iOS, Android, and HarmonyOS**, together with open-source on-device deployment support. It also provides **day-0 support for chip platforms from Rockchip, Houmo, Intel, AMD, and Arm**, extending deployment options across mobile and local computing devices. - 🛠️ **Developer Friendly.** MiniCPM-V 4.7 supports inference

• sam3.1↗

  AEmotionStudio

• SambaLingo-Thai-Base-i1-GGUF↗

  mradermacher

• Nemotron-Labs-Diffusion-3B↗

  NVIDIA

  Nemotron-Labs-Diffusion is a tri-mode language model that supports both AR decoding and diffusion-based parallel decoding by simply switching the attention pattern of the same model during inference. The synergy between these two modes enables a third mode, called self-speculation: the same model performs diffusion-based parallel drafting and AR verification with shared KV cache, achieving high acceptance lengths and decoding efficiency. The seamless mode switching by simply changing attention patterns enables high efficiency at different concurrency levels in varying deployment scenarios with one single model. - SOTA 3B, 8B, 14B dense LM family (base, instruct, and vision-language variants) supporting AR, diffusion, and self-speculation with the focus on decode efficiency. - Generation moved from a memory-bound regime toward a compute-bound regime. Model weights are loaded once and reused to compute multiple tokens during generation. - Self-speculation uses diffusion for drafting and AR for verification, providing a stronger alternative to MTP approaches: * 3x higher acceptance length and 2.2x speed-up vs. Qwen3-8B-Eagle3 in SGLang. * 5.9× tokens per forward over Qwen3-8B (no MTP) with the same accuracy. - Real-device speed-up across platforms: * DGX Spark (8B, concurrency 1): 2.7x faster with 112 tok/sec vs. 41.8 tok/sec AR using w4a16. * GB200 (8B, concurrency 1): 3.3x faster with 850 tok/sec vs. 253 tok/sec AR and 360 tok/sec Eagle3. Custom CUDA kernels boost to 1015 tok/sec (4x). - Diffusion speedup-of-light analysis shows that throughput can be further doubled (vs. current best) for a single user with better sampling - future research. Use of this model is governed by the NVIDIA Nemotron Open Model License.

• mlfoundations-dev_-_mistral_alpaca_sft_sample_2-gguf↗

  RichardErkhov

• Qwen2.5-VL-32B-Instruct-GGUF↗

  unsloth

  In addition to the original formula, we have further enhanced Qwen2.5-VL-32B's mathematical and problem-solving abilities through reinforcement learning. This has also significantly improved the model's subjective user experience, with response styles adjusted to better align with human preferences. Particularly for objective queries such as mathematics, logical reasoning, and knowledge-based Q&A, the level of detail in responses and the clarity of formatting have been noticeably enhanced. In the past five months since Qwen2-VL’s release, numerous developers have built new models on the Qwen2-VL vision-language models, providing us with valuable feedback. During this period, we focused on building more useful vision-language models. Today, we are excited to introduce the latest addition to the Qwen family: Qwen2.5-VL. We extend dynamic resolution to the temporal dimension by adopting dynamic FPS sampling, enabling the model to comprehend videos at various sampling rates. Accordingly, we update mRoPE in the time dimension with IDs and absolute time alignment, enabling the model to learn temporal sequence and speed, and ultimately acquire the ability to pinpoint specific moments.

• Cyber-Tiel-Coder-35B-A3B-APEX-I-NanoPlus-GGUF↗

  IsValorum

  If you consider these releases to be of high quality and they help you in your day-to-day workflow, consider supporting on Ko-fi. Every contribution directly sustains future cloud compute hours and helps support my university studies. > These are complementary APEX-I releases, not alternate downloads of the same model. Each receives the same surgical tensor-by-tensor approach and a design suitable for full or partial system-RAM inference: > - **Cyber-Tiel-Coder-35B-A3B APEX-I-MiniPlus-V2.1** — specialist uncensored agentic coding MoE tuned for high-speed local development (14.75 GB / Q5–Q6 quality).

• Juggernaut-XI-v11↗

  RunDiffusion

  The latest evolution of the Juggernaut SDXL line — sharper aesthetics, better hands, faces, and prompt adherence. > Juggernaut XI is the **latest evolution** of the Juggernaut SDXL line. Like Juggernaut X, it was **trained from the ground up** with **GPT-4 Vision** captioning — but with another pass of dataset cleanup, refined shot-type classification, and improved aesthetic targeting on top. Sharper hands, faces, eyes, and composition; stronger prompt adherence; native text rendering; and the same **RunDiffusion Photo** detail refinement that made earlier versions popular. - 🎨 **Massively improved aesthetics** — refined lighting, color, and composition vs. v10 - 🖐️ **Better hands, eyes, faces, and overall human anatomy** - 🎯 **Even stronger prompt adherence** — XI does what you ask with less wrestling than X - 📸 **Cleaner shot-type classification** — Full Body / Mid Shot / Portrait distinctions are crisper - 🔤 **Native text rendering** — legible signage, labels, short phrases - 💬 **Two prompting styles** — Natural language *or* Tagging (use whichever fits) - 🖼️ **RunDiffusion Photo** refinement for fine detail