malayalam-bert-finetuned-ner
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ICD-10-Code-Prediction
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View details ↗colSmol-256M-merged
akshayballal基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗EarthDial_4B_RGB
akshaydudhane基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗bhasha-embed-v0
AkshitaS基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗> [!CAUTION] > **Research artifact. Safety alignment has been deliberately removed.** This > model will attempt to comply with harmful, dangerous, illegal, and unethical > requests that the base model refuses, with no content moderation. See > **Safety**. **Provided for research purposes only, with no warranty and no > liability accepted by the author — see the disclaimer at the bottom.** An INT8 **W8A8** quantization of **Qwen3.8-27B-heretic** (the abliterated BF16 build of Qwen/Qwen3.8-27B), for fast serving on Ampere-class (SM 8.0) GPUs where native FP8 is unavailable. The MTP head is unquantized but is **not** byte-identical to the BF16 build's: it carries the same residual-stream rotation and RMSNorm fold as the rest of the model, so the two heads are not interchangeable. Its RMSNorm weights are stored as zeros, which is the identity scale under Qwen3.5's `(1 + weight)` RMSNorm.
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View details ↗124M-ChatML-Final
al20312基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗llama-2-tiny-random-GGUF
aladar基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗mms-tts-div-finetuned-md-f01
alakxenderThis is a fine-tuned VITS (Variational Inference with adversarial learning for end-to-end Text-to-Speech) model for Divehi speech synthesis. The model produces female voice audio from Thaana-scripted Divehi text. Fine-tuned from Meta’s MMS-TTS architecture using a curated dataset of synthetic Divehi speech. - **Model**: `alakxender/mms-tts-div-finetuned-md-f01` - **Evaluated Samples**: 3 - **Avg Estimated MOS (UTMOS)**: `2.349` - **Artifacts**: - 🎵 Audio: `outputs/audio/` - 📊 Spectrograms: `outputs/spectrograms/` - 📄 Report: `outputs/report.txt` - 📈 MOS Scores: `outputs/mos_scores.txt`
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View details ↗mms-tts-div-ft-spk01-f01
alakxender基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗w2v-bert-2.0-dhivehi-cv
alakxenderThis model is a fine-tuned version of alakxender/w2v-bert-2.0-dhivehi-cv on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3580 - Wer: 0.4591 The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 - mixed_precision_training: Native AMP
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View details ↗whisper-small-dv-full
alakxender基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Mistral-Small-3.1-DRAFT-0.5B
alamios基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗mt5-large-finetuned-mnli-xtreme-xnli
alan-turing-institute基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗TabSTAR
alana89**Paper:** TabSTAR: A Foundation Tabular Model With Semantically Target-Aware Representations > While deep learning has achieved remarkable success across many domains, it > has historically underperformed on tabular learning tasks, which remain > dominated by gradient boosting decision trees (GBDTs). However, recent > advancements are paving the way for Tabular Foundation Models, which can > leverage real-world knowledge and generalize across diverse datasets, > particularly when the data contains free-text. Although incorporating language > model capabilities into tabular tasks has been explored, most existing methods > utilize static, target-agnostic textual representations, limiting their > effectiveness. We introduce TabSTAR: a Foundation Tabular Model with > Semantically Target-Aware Representations. TabSTAR is designed to enable > transfer learning on tabular data with textual features, with an architecture > free of dataset-specific parameters. It unfreezes a pretrained text encoder and > takes as input target tokens, which provide the model with the context needed > to learn task-specific embeddings. TabSTAR achieves state-of-the-art > performance for both medium- and large-sized datasets across known benchmarks > of classification tasks with text features, and its pretraining phase exhibits > scaling laws in the number of datasets, offering a pathway for further > performance improvements.
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