The 1017 Q2 and matching BF16 vision encoder come from deepseek-ai/DeepSeek-V4.1-Flash-1017. They use the DwarfStar GGUF format. Pair the 1017 encoder with the 1017 language model; the original release remains available below. The 1017 files were tested on AMD gfx1151 with ROCm 10.0, using kyuz0/ds4 with these source overlays in order: 1. Bounded decoder replay. 2. Raw completion prompts. 3. Loaded-model modalities.
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View details ↗.qz-wrap { background: #0d1117; color: #e6edf3; font-family: 'Inter', system-ui, -apple-system, sans-serif; font-size: 1rem; line-height: 1.7; padding: 2rem 2.5rem; border-radius: 8px; margin-bottom: 2rem; } .qz-wrap * { color: #e6edf3; } .qz-wrap a { color: #58a6ff; text-decoration: none; } .qz-wrap a:hover { text-decoration: underline; } .qz-header { font-size: 2.2rem; font-weight: 700; color: #ffffff; border-bottom: 3px solid #22d3ee; padding-bottom: 0.5rem; margin-bottom: 1rem; } .qz-subtitle { font-size: 1.05rem; color: #8b949e; margin-top: 0.2rem; margin-bottom: 0.4rem; } .qz-section { font-size: 1.3rem; font-weight: 600; color: #ffffff; margin-top: 2rem; margin-bottom: 0.6rem; } .qz-sub { font-size: 1.1rem; font-weight: 600; color: #7ab8d4; margin-top: 1.4rem; margin-bottom: 0.5rem; } .qz-wrap table.qz-table { display: table; width: 100%; min-width: 100%; max-width: 100%; table-layout: fixed; border-collapse: collapse; border-spacing: 0; margin-bottom: 1rem; }
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View details ↗This is an ONNX version of the Manga OCR model, designed for optical character recognition of Japanese text, with a primary focus on manga. This model is based on the original work by kha-white/manga-ocr, kha-white/manga-ocr-base and modification by jzhang533/manga-ocr-base-2025. The models in this repository were exported to the ONNX format using Hugging Face Optimum. Manga OCR utilizes the Vision Encoder Decoder framework. It is designed to be a high-quality text recognition tool, robust against various scenarios specific to manga: - Both vertical and horizontal text - Text with furigana - Text overlaid on images - A wide variety of fonts and font styles - Low-quality images
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View details ↗This is a HindSBERT model ( l3cube-pune/hindi-sentence-bert-nli ) fine-tuned on the STS dataset. Released as a part of project MahaNLP : https://github.com/l3cube-pune/MarathiNLP A multilingual version of this model supporting major Indic languages and cross-lingual sentence similarity is shared here indic-sentence-similarity-sbert More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2211.11187) Other Monolingual similarity models are listed below: Marathi Similarity Hindi Similarity Kannada Similarity Telugu Similarity Malayalam Similarity Tamil Similarity Gujarati Similarity Oriya Similarity Bengali Similarity Punjabi Similarity Indic Similarity (multilingual)
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View details ↗This is a IndicSBERT model ( l3cube-pune/indic-sentence-bert-nli ) trained on the STS dataset of ten major Indian Languages. The single model works for English, Hindi, Marathi, Kannada, Tamil, Telugu, Gujarati, Oriya, Punjabi, Malayalam, and Bengali. The model also has cross-lingual capabilities. Released as a part of project MahaNLP: https://github.com/l3cube-pune/MarathiNLP Generic Indic Sentence BERT model is shared here : l3cube-pune/indic-sentence-bert-nli More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2304.11434) Other Monolingual similarity models are listed below: Marathi Similarity Hindi Similarity Kannada Similarity Telugu Similarity Malayalam Similarity Tamil Similarity Gujarati Similarity Oriya Similarity Bengali Similarity Punjabi Similarity Indic Similarity (multilingual)
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