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AI 모델 84759

• OmniAvatar-14B↗

  OmniAvatar

• MiniCPM-V-4.7-1B-gguf↗

  openbmb

  > **This repository hosts the GGUF (llama.cpp) quantized version of MiniCPM-V 4.7 1B.** For the original BF16 weights and the full model card, please refer to openbmb/MiniCPM-V-4.7-1B. Two sets of files are provided. **They differ only in the embedded chat template** — the tensor weights are byte-identical. - **Root files** — thinking is disabled inside the embedded template and cannot be turned on. This is the recommended default for this model, which is not trained as a reasoning model. - **`thinking/` files** — the official upstream template is restored, so the model emits a `[Start thinking] … [End thinking]` trace before answering. Useful when you want to see the reasoning steps; note that it multiplies the token count, so set a larger `-n`.

• OpenMed-NER-GenomeDetect-BigMed-560M↗

  OpenMed

  **Specialized model for Gene/Protein Entity Recognition - Gene and protein mentions** This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for gene/protein entity recognition - gene and protein mentions. This specialized model excels at identifying and extracting biomedical entities from clinical texts, research papers, and healthcare documents, enabling applications such as **drug interaction detection**, **medication extraction from patient records**, **adverse event monitoring**, **literature mining for drug discovery**, and **biomedical knowledge graph construction** with **production-ready reliability** for clinical and research applications. - **High Precision**: Optimized for biomedical entity recognition - **Domain-Specific**: Trained on curated BC2GM dataset - **Production-Ready**: Validated on clinical benchmarks - **Easy Integration**: Compatible with Hugging Face Transformers ecosystem

• DeepSeek-R1-Distill-Qwen-14B-MLX-4bit↗

  Outlier-Ai

• VibeThinker-3B-GSQ-RCO-GGUF↗

  pfeifferj

• Qwen3-8B-abliterated-GGUF↗

  PocketWeights

• Ternary-Bonsai-2-4B-mlx-2bit↗

  prism-ml

  > **PrismML-Eng/Bonsai-demo is the source of truth for running these models.** Where anything here disagrees with it, it is right. For an OpenAI-compatible server, run `python -m mlx_vlm.server --model bonsai2-4b-mlx` and send the sampling settings in each request. For CUDA, CPU, and llama.cpp on Metal, use the GGUF packs of the same weights: **Ternary-Bonsai-2-4B-gguf**.

• microsoft-AesCode-8B-GGUF↗

  prithivMLmods

  > **AesCode-8B** is Microsoft's 8B vision-language model, built on Qwen3-VL-8B-Instruct, for generating information-rich visual artifacts such as slides, posters, dashboards and reports as structured, editable HTML/CSS. It addresses a gap between two kinds of generator: code models can't see how layout, hierarchy and color come together on the canvas, while image generators compose attractive pages but misrender text, numbers and logical relationships. AesCode therefore takes an image generated from the same prompt as an aesthetic reference and follows the prompt for the required content. Tables come out as HTML table structures and charts as ECharts specifications, so both stay inspectable and verifiable. Training was cold-start SFT on 3,000 demonstrations, followed by GDPO reinforcement learning for 400 steps over 7,408 prompts, using verl and no separately trained reward model. Each target is represented as a design graph, and the reward is split into seven decoupled channels: execution, text, boundary, tablechart, layout, whitespace and design. Deterministic verifiers score what can be parsed from the code and its rendering, and a VLM judge scores the rest against a sample-specific Visual Graph Rubric. On the card's 300 infographic samples, AesCode-8B scores 82.94 Overall (Rule 90.07, Visual 75.80). That is above the reference-conditioned GPT-5.5 (81.28) and Claude Opus 4.8 (80.39), and 31.0 points above its reference-conditioned Qwen3-VL-8B backbone. The 32B companion model leads at 85.89. Style is a ceiling for every system, with no model above 60. The reference image is optional, since withholding it costs AesCode-8B only 1.00 Visual point, versus 19.55 for the base model, though quality is highest with it. The model loads through standard Transformers or vLLM, with the training code, verifier stack and rubric builder released on GitHub. The paper is still listed as under review. It is released under Apache 2.0.

• speech-separation-ami-1.0↗

  pyannote

• InkubaLM-0.4B-GGUF↗

  QuantFactory