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AI 모델 2119

  • Moonlight-16B-A3B↗

    月之暗面 · Kimi

    Recently, the Muon optimizer has demonstrated strong results in training small-scale language models, but the scalability to larger models has not been proven. We identify two crucial techniques for scaling up Muon: - **Weight Decay**: Critical for scaling to larger models - **Consistent RMS Updates**: Enforcing a consistent root mean square on model updates These techniques allow Muon to work out-of-the-box on large-scale training without the need of hyper-parameter tuning. Scaling law experiments indicate that Muon is $\sim2\times$ more sample efficient than Adam with compute optimal training.

  • Sharp-MiniCPM5-2B-GGUF↗

    peculiar-ragdoll

    Dynamic **imatrix** quants of openbmb/MiniCPM5-2B, a 2.5B dense 131k-context model, carrying our cyber-and-coding-weighted importance matrix, an unsloth-dynamic-style per-tensor quant policy, and the **Sharp-MiniCPM** chat template with fixes and an improved system prompt. We recommend the Q6_K_XL for anyone who can run it. Practically lossless, at under half the size of the F16 model, and scoring higher than stock Q8_0 on SWE-bench-Live. **99% KL** is the 99th percentile of KL divergence against the BF16 weights on held-out code — the rare token where a quant actually diverges. **top-1** is how often the quant picks the same next token as BF16. The last three columns are the largest `q8_0` KV context that fits beside the weights on a 4 / 6 / 8 GB card.

  • Winnow-E4B↗

    EldanRing

    Winnow-E4B is a compact EldanRing fine-tune of Gemma 4 E4B IT for typed decisions, chat, and image input. Give it a shared state and questions with known answer options; it scores the candidates without generating an explanation. Typical uses include routing requests, choosing actions, checking conditions, and rating urgency. The GGUF downloads contain the merged fine-tune. No separate adapter or base-model download is needed. Image input uses the matching vision projector. These published Q8 results compare the models on the same questions. Jev and Kev report top-choice accuracy; Typed reports agreement with synthetic teacher labels.

  • mask2former-swin-tiny-cityscapes-instance↗

    facebook

    Mask2Former model trained on Cityscapes instance segmentation (tiny-sized version, Swin backbone). It was introduced in the paper Masked-attention Mask Transformer for Universal Image Segmentation and first released in this repository. Disclaimer: The team releasing Mask2Former did not write a model card for this model so this model card has been written by the Hugging Face team. Mask2Former addresses instance, semantic and panoptic segmentation with the same paradigm: by predicting a set of masks and corresponding labels. Hence, all 3 tasks are treated as if they were instance segmentation. Mask2Former outperforms the previous SOTA, MaskFormer both in terms of performance an efficiency by (i) replacing the pixel decoder with a more advanced multi-scale deformable attention Transformer, (ii) adopting a Transformer decoder with masked attention to boost performance without without introducing additional computation and (iii) improving training efficiency by calculating the loss on subsampled points instead of whole masks.

  • openjev-FP8↗

    openjev

    The FP8 file of OpenJev: the same weights stored as FP8 (e4m3, 128×128 weight blocks, dynamic activations), about 29 GB instead of 54 GB. Everything about the model, the API and the results is in the main card; this page only says how this file was checked and how to run it. A one-pass readout lives on small score differences, so the file was re-run on the same frozen tests as the 16-bit model, with the same helper and the same fixed calibration: About 2% of individual answers change between the two files (219 of 10,000 text answers, 40 of 2,000 screenshot answers), in both directions. The rule fixed before the run was "no more than 0.5 points below the served model"; both tests pass it.

  • StartLux-Decision-v1-4B-Q8_0-GGUF↗

    startlux-models

    StartLux-Decision-v1-4B in Q8_0 (8-bit) as a GGUF file for llama.cpp. The decision server in this repository turns it into the same typed decisions, with a probability for every option, as the original model. "Same decision" is the share of the 231 public JevBench items on which the file picks the same answer as the original weights run through the `startlux_decision` package, with the same prompts, readout and temperatures. Q8_0 and Q4_K_M are llama.cpp's standard quantizations of BF16, without an importance matrix. `-ngl 99` puts every layer on the GPU (CUDA or Metal); leave it out on a CPU-only machine. llama.cpp has to be recent enough for this model (build b10454 or newer).

  • Mini-InternVL-Chat-2B-V1-5↗

    OpenGVLab

    We are delighted to introduce the Mini-InternVL-Chat series. In the era of large language models, many researchers have started to focus on smaller language models, such as Gemma-2B, Qwen-1.8B, and InternLM2-1.8B. Inspired by their efforts, we have distilled our vision foundation model InternViT-6B-448px-V1-5 down to 300M and used InternLM2-Chat-1.8B or Phi-3-mini-128k-instruct as our language model. This resulted in a small multimodal model with excellent performance. As shown in the figure below, we adopted the same model architecture as InternVL 1.5. We simply replaced the original InternViT-6B with InternViT-300M and InternLM2-Chat-20B with InternLM2-Chat-1.8B / Phi-3-mini-128k-instruct. For training, we used the same data as InternVL 1.5 to train this smaller model. Additionally, due to the lower training costs of smaller models, we used a context length of 8K during training. - Architecture: InternViT-300M-448px + MLP + InternLM2-Chat-1.8B - Image size: dynamic resolution, max to 40 tiles of 448 x 448 (4K resolution). - Params: 2.2B

  • swin-tiny-patch4-window7-224-finetuned-mri↗

    raedinkhaled

    Swin-Tiny fine-tuned to classify cardiac MRI images as `cad` (coronary artery disease) or `healthy`. This checkpoint comes from the co-authored M.Sc. thesis *Novel Deep Learning Architecture for Predicting Heart Diseases based on Transformers and Attention Mechanism with Explainability Model* by Raedin Khaled Sakhri and Loutfi Boufeligha, supervised by Prof. Abdelouaheb Moussaoui (Ferhat Abbas University, Algeria, July 2022). The thesis compared eight models on the same task. This one is the fine-tuned Swin-Tiny, pretrained on ImageNet-1k. The Trainer logged about 6.1 hours of training for the 3 epochs; the thesis rounds this to about 7 hours. For comparison, in the thesis: ViT-Base 98.27%, DeiT 99.01% (overfitting noted), a Vision Transformer trained from scratch 75.73%, and a CNN 88%.

  • Florence-2-large↗

    florence-community

    > [!NOTE] > This is the repository for official transformers converted checkpoint of Microsoft's Florence model. **This is a continued pretrained version of Florence-2-large model with 4k context length, only 0.1B samples are used for continue pretraining, thus it might not be trained well. In addition, OCR task has been updated with line separator ('\n'). COCO OD AP 39.8** Florence-2 is an advanced vision foundation model that uses a prompt-based approach to handle a wide range of vision and vision-language tasks. Florence-2 can interpret simple text prompts to perform tasks like captioning, object detection, and segmentation. It leverages our FLD-5B dataset, containing 5.4 billion annotations across 126 million images, to master multi-task learning. The model's sequence-to-sequence architecture enables it to excel in both zero-shot and fine-tuned settings, proving to be a competitive vision foundation model.

  • gemma-4-12B-it-qat-q4_0-unquantized↗

    Google

    > [!Note] > This model card is for the new versions of the Gemma 4 family optimized with Quantization-Aware Training (QAT), which allows preserving similar quality to bfloat16 while dramatically reducing the memory requirements to load the model. > Four versions of the QAT checkpoints are available: > * **Unquantized QAT checkpoints** (Q4_0): Half-precision weights extracted from the QAT pipeline, ideal for custom downstream compilation and research. Available for Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B, and their drafter models. > * **GGUF** (Q4_0): Ready-to-deploy formats for broad ecosystem compatibility. Available for Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B. > * **Mobile-optimized** (wNa8o8): A custom schema engineered explicitly for mobile hardware efficiency. It features targeted 2-bit decoding layers, optimized KV caches, and static activations to maximize VRAM savings. Available for Gemma 4 E2B and E4B. > * **Compressed Tensors** (w4a16): QAT checkpoints serialized in the compressed-tensors format for native, optimized inference with vLLM. Available for Gemma 4 E2B, E4B, 12B, and 31B. > * **Assistant Compatibility**: When using multi-token prediction (speculative decoding) with an assistant model alongside a QAT target model, the assistant model must also be a QAT checkpoint with the same precision to ensure compatibility. Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on E2B, E4B, and 12B) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages. Featuring both Dense and Mixture-of-Experts (MoE) architectures, Gemma 4 is well-suited for tasks like text generation, coding, and reasoning. The models are available in five distinct sizes: **E2B**, **E4B**, **12B**, **26B A4B**, and **31B**. Their diverse sizes make them deployable in environments ranging from high-end phones to laptops and servers, democratizing access to state-of-the-art AI.