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AI モデル 2119
- orcarouter_GLM-5.3-Flash-Uncensored-GGUF↗
BoldingBuilds
GGUF quantizations of **orcarouter/GLM-5.3-Flash-Uncensored-FP8**, built with a custom importance matrix and measured rather than assumed. > ⚠️ **Uncensored model.** Safety refusals were removed by OrcaRouter. It will comply with > harmful, unethical, and illegal requests. Released for research — red-teaming, refusal- > mechanism study, robustness evaluation. You are responsible for what you do with it. > 🔧 **What happened, and where it stands (2026-09-06).** On 2026-09-05 a user reported > looping and incoherent output from `IQ2_S` (discussion #1). They were > right. The original build pinned only the `blk.45` MTP block at high precision; attention, > the **shared** experts, the three dense FFN blocks, and the embeddings were all quantized > down with the routed experts. The shared experts and attention run on every token. > > **`IQ2_S` was rebuilt on 2026-09-06** with those tensors held at `q8_0` (+7 GB) and tested > against the original on a rented H200: 12 tasks × 2 seeds × 2 samplers, same scorer, same > settings. Full table in **Coherence gate** below. Short version: the original failed 3 of 8 > coding tasks by reasoning to the output cap without ever answering; the rebuild passed > all 48, matching `IQ3_XXS`, which also passed all 48 on its original recipe. > **`IQ2_XXS` is being rebuilt the same way** and stays flagged until it passes the same gate. > > **Two corrections to earlier versions of this page.** The imatrix has **10 chunks, not 60** > (the run checkpointed at 10 and stopped; numerically sound, one uncovered expert row in > `blk.29`). And the repetition-penalty workaround I posted **did not change the outcome** on > this failure mode — 21/24 → 21/24 for the original file with and without it. It helps > n-gram repetition; it does not help a model that cannot emit the literal it intends. > The rebuilt files carry `repeat_penalty 1.1` in their metadata anyway, because it is cheap. > > Only `IQ3_XXS` had been benchmarked before publication, and I shipped all three off that. > That was the actual mistake. The gate below is the fix for the process, not just the file.
- sam-vit-base↗
facebook
0. TL;DR 1. Model Details 2. Usage 3. Citation The **Segment Anything Model (SAM)** produces high quality object masks from input prompts such as points or boxes, and it can be used to generate masks for all objects in an image. It has been trained on a dataset of 11 million images and 1.1 billion masks, and has strong zero-shot performance on a variety of segmentation tasks. The abstract of the paper states: > We introduce the Segment Anything (SA) project: a new task, model, and dataset for image segmentation. Using our efficient model in a data collection loop, we built the largest segmentation dataset to date (by far), with over 1 billion masks on 11M licensed and privacy respecting images. The model is designed and trained to be promptable, so it can transfer zero-shot to new image distributions and tasks. We evaluate its capabilities on numerous tasks and find that its zero-shot performance is impressive -- often competitive with or even superior to prior fully supervised results. We are releasing the Segment Anything Model (SAM) and corresponding dataset (SA-1B) of 1B masks and 11M images at https://segment-anything.com to foster research into foundation models for computer vision.
- PP-DocBlockLayout↗
PaddlePaddle
A layout block localization model trained on a self-built dataset containing Chinese and English papers, PPT, multi-layout magazines, contracts, books, exams, ancient books and research reports using RT-DETR-L. The layout detection model includes 1 category: Region. **Note**: the evaluation set of the above precision indicators is the self built version sub area detection data set, including Chinese and English papers, magazines, newspapers, research reports PPT、 1000 document type pictures such as test papers and textbooks. You can also integrate the model inference of the LayoutDetection module into your project. Before running the following code, please download the sample image to your local machine.
- PP-OCRv6_medium_det↗
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.
- Qwen3.8-27B-NVFP4↗
RedHatAI
This model is an updated quantized version of Qwen/Qwen3.8-27B, using a mixed-precision FP4/FP8 scheme with an unquantized language-model head and updated quantization scales. See Evaluation for accuracy results. This model was produced by applying mixed-precision quantization to Qwen/Qwen3.8-27B. MLP projections are quantized to FP4, attention projections and the final MLP layers are quantized to FP8, and the KV cache is quantized to FP8, while the language-model head is kept in full precision to preserve output quality. The quantization scales were updated by calibrating on a 512-sample subset of the perfectblend dataset with a recipe that combines AWQ and GPTQ. Only the weights and activations of the linear operators within the transformer blocks are quantized using LLM Compressor. The checkpoint is ~24.7 GB on disk (versus ~54 GB in BF16), reducing disk size and GPU memory requirements by roughly 70%.
- Qwen3.8-27B-GPTQ-INT4-FP8KV↗
abhishekchohan
A 4-bit weight-only **GPTQ** quantization of **Qwen/Qwen3.8-27B** with an **fp8-e4m3 KV cache calibrated alongside the weights**. INT4 weights shrink the footprint ≈2.8× and fp8 KV halves the cache, so the model serves its native **262,144-token** context on a **single 48 GB GPU**. fp8 KV also makes YaRN extension to **524,288 tokens** fit the same card, and up to 1M tokens on 96 GB (retrieval beyond 262k unvalidated). Identical harness and hardware for base and quantized: lm-evaluation-harness with the vLLM 0.27.1 backend on a single NVIDIA RTX PRO 6000 (Blackwell, 96 GB), greedy decoding, seed 1234. The base runs with bf16 KV and the quantized model with fp8-e4m3 KV, each at its recommended serving configuration, so every Δ measures the full int4 + fp8-KV stack rather than weights alone. Generative tasks run with the model's native chat template, thinking enabled, `max_gen_toks=8192`; loglikelihood tasks run untemplated. `minerva_math500` reports `math_verify`, since strict `exact_match` measures answer format, not content, on thinking-model outputs. The BF16 base scores identically (9/9), showing no retrieval degradation through the int4 + fp8-KV stack at the context length it was calibrated at.
- vit_base_patch16_224.sam_in1k↗
timm
- Swift-1.5-Qwen3.8-27B-heretic-GSQ-RCO-GGUF↗
jegreene
This is a **partially decensored** build of UkisAI's Swift 1.5 Qwen3.8-27B, made with Heretic. It is packaged as a single-file llama.cpp GGUF and quantized the same way as UkisAI's own GSQ-RCO `-mtp` releases, using the same per-tensor allocations and the same imatrix. Three sizes are provided: IQ3_S, IQ3_XXS and IQ2_S. It includes the **MTP head**, for speculative decoding, and a **vision projector** (mmproj) built from Swift's own vision tower. > **Not an official UkisAI release.** UkisAI and Alibaba Cloud do not endorse it. > "Swift" is used only to describe where the model comes from.
- Xiaomi-OCR-0↗
SeerRay-Lab
Xiaomi-OCR-0 A unified 0.8B model for document parsing and OCR-related understanding. English · 简体中文 Xiaomi-OCR-0 is a unified 0.8B OCR-specialized vision-language model for both **document parsing** and **OCR-centric understanding**. Starting from Qwen3.5-0.8B-Base, Xiaomi-OCR-0 is trained on an approximately 170M-sample OCR-centric corpus with a progressive training recipe:
- CommunityForensics-DeepfakeDet-ViT↗
buildborderless
Vision Transformer (ViT-Small) trained on 2.7M samples across 4,803 generators for detecting AI-generated images. Presented in Community Forensics: Using Thousands of Generators to Train Fake Image Detectors (CVPR 2025). **Uploaded for community validation as part of OpenSight** — An upcoming open-source framework for adaptive deepfake detection. **Project OpenSight HF Spaces coming soon with an eval playground and eventually a leaderboard. Preview:**