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AI 모델 85003
- 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
- Qwen3-1.7B-MLX-bf16↗
阿里巴巴 · Qwen
- SAE-Res-Qwen3-30B-A3B-Base-W128K-L0_100↗
阿里巴巴 · Qwen
- Phi-3-mini-4k-instruct-iMat-GGUF↗
qwp4w3hyb
- Llama-3.2-1B-Amharic-Instruct↗
rasyosef
- Meta-Llama-3.1-8B-FP8↗
RedHatAI
Quantized version of Meta-Llama-3.1-8B. It achieves an average score of 65.90 on the OpenLLM benchmark (version 1), whereas the unquantized model achieves 66.47. This model was obtained by quantizing the weights and activations of Meta-Llama-3.1-8B to FP8 data type, ready for inference with vLLM built from source. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. Only the weights and activations of the linear operators within transformers blocks are quantized. Symmetric per-tensor quantization is applied, in which a single linear scaling maps the FP8 representations of the quantized weights and activations. LLM Compressor is used for quantization with 512 sequences of UltraChat.
- Trinity-Large-Thinking-NVFP4↗
RedHatAI
- BramVanroy_-_llama2-13b-ft-mc4_nl_cleaned_tiny-gguf↗
RichardErkhov