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AI Models 84760

• Qalam_Net_V2↗

  Ali0044

  **Qalam-Net V2** (قلم-نت) is a high-performance Arabic Optical Character Recognition (OCR) system. Built on the **TrOCR** (Transformer-based OCR) architecture, it achieves superior accuracy by treats OCR as a sequence-to-sequence problem, mapping visual features directly to text tokens. The model utilizes a **Vision-Encoder-Decoder** framework, specifically optimized for the complexities of Arabic script (ligatures, cursive nature, and right-to-left orientation). - **End-to-End Transformer**: No reliance on traditional CNN-RNN architectures or complex preprocessing (like line segmentation). - **Arabic Script Specialist**: Fine-tuned on the `mssqpi/Arabic-OCR-Dataset` for robust handling of various Arabic fonts and styles. - **State-of-the-Art Accuracy**: Leverages pre-trained vision and language weights from `microsoft/trocr-base-handwritten`. - **Flexible Deployment**: Supports CUDA, MPS (Apple Silicon), and CPU execution.

• Molmo-72B-0924↗

  allenai

  Molmo is a family of open vision-language models developed by the Allen Institute for AI. Molmo models are trained on PixMo, a dataset of 1 million, highly-curated image-text pairs. It has state-of-the-art performance among multimodal models with a similar size while being fully open-source. You can find all models in the Molmo family here. **Learn more** about the Molmo family in our announcement blog post or the paper. Molmo 72B is based on Qwen2-72B and uses OpenAI CLIP as vision backbone. Molmo-72B achieves the highest academic benchmark score and ranks second on human evaluation, just slightly behind GPT-4o. This checkpoint is a **preview** of the Molmo release. All artifacts used in creating Molmo (PixMo dataset, training code, evaluations, intermediate checkpoints) will be made available at a later date, furthering our commitment to open-source AI development and reproducibility.

• olmOCR-7B-0225-preview↗

  allenai

  This is a preview release of the olmOCR model that's fine tuned from Qwen2-VL-7B-Instruct using the olmOCR-mix-0225 dataset. Quick links: - 📃 Paper - 🤗 Dataset - 🛠️ Code - 🎮 Demo The best way to use this model is via the olmOCR toolkit. The toolkit comes with an efficient inference setup via sglang that can handle millions of documents at scale.

• tulu-30b↗

  allenai

• sanoTTS↗

  ampixa

  ***sano*** (सानो) — Nepali for **"small."** A family of tiny neural text-to-speech voices — **294k to 2.27M parameters** — that run with **no cloud and no NPU**: real-time on a ~$3 ESP32-S3 (out a GPIO into an LM386 and a speaker), or live in the browser via WASM. **Live demo:** tts.ampixa.com/sanoTTS — every voice synthesizes your text live in the browser, no server, no upload. Python needs `sanotts` >= 0.3.0, the browser `sanotts-web` >= 0.3.0. Both fall back to the GitHub releases or the Pages host if Hugging Face cannot be reached, so an outage here cannot break an install. Python packages land in `~/.cache/sanotts/`; set `SANOTTS_VOICE_SOURCE=hf` or `=github` to pin one host. In the browser, passing `voiceBase` yourself turns the fallback off, so a self-hosted deployment never quietly reaches back to our servers.

• Flux-Klein-NSFW-Lora↗

  AntiLeecher

• Qwen3.8-27B-ROCmFPX↗

  ArtomYuan

  **ROCmFPX quantized GGUF (7 tiers + vision mmproj)** of Qwen/Qwen3.8-27B — official BF16 weights, locally requantized. This repository uses strix.cpp, a llama.cpp fork **specifically tuned for the AMD Strix Halo `gfx1151` (Ryzen AI MAX+ 395) UMA architecture and ROCm/HIP backend**, with native support for the ROCmFPX quantization format. Requires strix.cpp (this fork supports ROCmFPX; **upstream llama.cpp cannot load these files** — format note at the end).

• Ethio-ASR-amharic↗

  badrex

• Qwen3.8-27B-Opus-Distill-GGUF↗

  barozp

• Ling-3.0-flash-GGUF↗

  bartowski

  **Model details:** - Parameter count: 127B - Input support: text - Speculative decoding: yes (MTP) - details - imatrix: yes - details - Perplexity/KLD measured: no **Don't know which to choose?** Grab Q4_K_M (77.80GB) - usually a good mix of size and performance. Download instructions available here The files marked `true` in the Split column above are stored as multiple parts in a folder. To download all the parts to a local folder, run: