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AI Models 2119

  • bert-base-greek-uncased-v1↗

    nlpaueb

    * The Greek part of Wikipedia, * The Greek part of European Parliament Proceedings Parallel Corpus, and * The Greek part of OSCAR, a cleansed version of Common Crawl. * The entire corpus of Greek legislation, as published by the National Publication Office, * The entire corpus of EU legislation (Greek translation), as published in Eur-Lex. * We trained BERT using the official code provided in Google BERT's GitHub repository (https://github.com/google-research/bert).* We then used Hugging Face's Transformers conversion script to convert the TF checkpoint and vocabulary in the desired format in order to be able to load the model in two lines of code for both PyTorch and TF2 users. * We released a model similar to the English `bert-base-uncased` model (12-layer, 768-hidden, 12-heads, 110M parameters). * We chose to follow the same training set-up: 1 million training steps with batches of 256 sequences of length 512 with an initial learning rate 1e-4. * We were able to use a single Google Cloud TPU v3-8 provided for free from TensorFlow Research Cloud (TFRC), while also utilizing GCP research credits. Huge thanks to both Google programs for supporting us!

  • MobileCLIP2-S2-OpenCLIP↗

    timm

    These weights and model card are adapted from the original Apple model at https://huggingface.co/apple/MobileCLIP2-S2. This version uses canonical OpenCLIP configs and weight naming. MobileCLIP2 was introduced in MobileCLIP2: Improving Multi-Modal Reinforced Training (TMLR August 2025 Featured ), by Fartash Faghri, Pavan Kumar Anasosalu Vasu, Cem Koc, Vaishaal Shankar, Alexander T Toshev, Oncel Tuzel, Hadi Pouransari. * `MobileCLIP2-S4` matches the accuracy of SigLIP-SO400M/14 with 2x fewer parameters and surpasses DFN ViT-L/14 at 2.5x lower latency measured on iPhone12 Pro Max. * `MobileCLIP-S3/S4` are our new architectures trained on MobileCLIP’s training dataset, DataCompDR-1B (dashed lines). * Our smallest variant `MobileCLIP-S0` obtains similar zero-shot performance as OpenAI's ViT-B/16 model while being 4.8x faster and 2.8x smaller. * `MobileCLIP-S2` obtains better avg zero-shot performance than SigLIP's ViT-B/16 model while being 2.3x faster and 2.1x smaller, and trained with 3x less seen samples. * `MobileCLIP-B (LT)` attains zero-shot ImageNet performance of **77.2%** which is significantly better than recent works like DFN and SigLIP with similar architectures or even OpenAI's ViT-L/14@336.

  • MiniCPM-V-4.7-35B-A3B-Uncensored-GGUF↗

    AtomicChat

    One refusal direction projected out of OpenBMB's original weights, shipped two ways: three baked GGUF builds, and a 106 MB LoRA adapter for the MiniCPM-V 4.7 quants you already have. The measurements behind this card are public. On held-out prompts, refusals fall from 96.3% to 0% in English, and from 13 of 30 to none with thinking on. MMLU moves by 0.25 points, inside the noise. You can now run the Uncensored MiniCPM-V 4.7 in Atomic Chat , locally on your own machine. The 20.1 GB build matches the original's token choice 94.5% of the time; our quant of the unmodified model at the same size does 94.9%. Already running our MiniCPM-V 4.7 quants ? Add the adapter with --lora-scaled instead of downloading a new model. *28 Oct 2026: each build is now a single file instead of 11 to 16 shards of 2 GB. The weights are unchanged (tensor-for-tensor identical).*

  • deepseek-vl-1.3b-chat↗

    deepseek-community

    Deepseek-VL was introduced by the DeepSeek AI team. It is a vision-language model (VLM) designed to process both text and images for generating contextually relevant responses. The model leverages LLaMA as its text encoder, while SigLip is used for encoding images. > We present DeepSeek-VL, an open-source Vision-Language (VL) Model designed for real-world vision and language understanding applications. Our approach is structured around three key dimensions: We strive to ensure our data is diverse, scalable, and extensively covers real-world scenarios including web screenshots, PDFs, OCR, charts, and knowledge-based content, aiming for a comprehensive representation of practical contexts. Further, we create a use case taxonomy from real user scenarios and construct an instruction tuning dataset accordingly. The fine-tuning with this dataset substantially improves the model's user experience in practical applications. Considering efficiency and the demands of most real-world scenarios, DeepSeek-VL incorporates a hybrid vision encoder that efficiently processes high-resolution images (1024 x 1024), while maintaining a relatively low computational overhead. This design choice ensures the model's ability to capture critical semantic and detailed information across various visual tasks. We posit that a proficient Vision-Language Model should, foremost, possess strong language abilities. To ensure the preservation of LLM capabilities during pretraining, we investigate an effective VL pretraining strategy by integrating LLM training from the beginning and carefully managing the competitive dynamics observed between vision and language modalities. The DeepSeek-VL family (both 1.3B and 7B models) showcases superior user experiences as a vision-language chatbot in real-world applications, achieving state-of-the-art or competitive performance across a wide range of visual-language benchmarks at the same model size while maintaining robust performance on language-centric benchmarks. We have made both 1.3B and 7B models publicly accessible to foster innovations based on this foundation model. Quantization reduces the memory burden of large models by representing the weights i

  • Spark-X2.5-4B-abliterated-FIT-GGUF↗

    SC117

    FIT-GGUF v0.2.0 GATE-VERIFIED TIERS 4 TIERS · 2.26–3.15 GiB ABLITERATED (T615) MEASURED KL + SAME-TOP APACHE-2.0 Spark-X2.5-4B-abliterated · FIT-GGUF Four fidelity tiers of a 1M-context hybrid-attention 4B — every shipped file re-verified against its own BF16, byte-exact and reproducible. 🧭 About FIT-GGUF — the tool behind these files Every file in this repository was planned, executed and verified by FIT-GGUF , an open-source, deterministic tensor-level planning layer on top of llama.cpp quantization. Standard GGUF quantization asks you to pick one of a handful of presets; FIT-GGUF instead asks what quality do you want , then finds and verifies the smallest GGUF that demonstrably meets it. Traditional GGUF gives you presets. FIT gives you a fidelity contract: macro KL ≤ tier anchor ∧ same-top ≥ model-calibrated floor. Deterministic size prediction & byte-exact delivery ✅ Validated (G2 gate, delta = 0) Universally optimal tensor allocation ⚠️ Not established — FIT claims verified fidelity contracts, not a universal quality optimum The method, the preregistered research record and the fit CLI are open source: github.com/Scorp1o117/FIT-GGUF ⚠️ Safety notice / 安全提示 The source model is an abliterated, refusal-removed model with no meaningful built-in guardrails, and may comply with harmful, illegal or unsafe requests. Use it only where you can provide appropriate moderation, access control and legal review. Do not deploy it to end users without your own safety layer. 源模型经过拒答方向消融，不具备可靠的内置安全护栏；请仅在合法、受控、具备审核与滥用防护的环境中使用，使用者自行承担部署责任。

  • 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%.