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TText generation▧Image generation▷Video generation⌘Code generation◖Speech synthesis♫Music generation⬡3D modeling▤Document analysis
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AuroLA-7B-PT

Jazzcharles

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A model for language detection with support for 45 languages This model was created by fine-tuning bert-base-multilingual-cased on the common language dataset. This dataset has support for 45 languages, which are listed below: This model was evaluated on the test split of the common language dataset, and achieved the following metrics: * Accuracy: 97.8%

Text analysis
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This checkpoint is a wav2vec2-large model that is useful for generating transcriptions with punctuation. It is intended for use in building transcriptions for TTS models, where punctuation is very important for prosody. This model was created by fine-tuning the `facebook/wav2vec2-large-robust-ft-libri-960h` checkpoint on the libritts and voxpopuli datasets with a new vocabulary that includes punctuation. The model gets a respectable WER of 4.45% on the librispeech validation set. The baseline, `facebook/wav2vec2-large-robust-ft-libri-960h`, got 4.3%.

Speech recognition
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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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Translation
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> **4-bit quantized GGUF** of Qwen/Qwen3.5-9B optimized for **on-device iOS inference** via llama.cpp. The most capable model you can run on an iPhone. This model is available directly in the HaploAI iOS app (v1.18+). Download it from the model selection page. The 9B model **beats Qwen3-80B** on GPQA Diamond (81.7 vs 77.2), IFEval (91.5 vs 88.9), and HMMT math (83.2 vs 73.7). It also **outperforms GPT-OSS-120B** on MMLU-Pro (82.5 vs 80.8) and GPQA Diamond (81.7 vs 80.1).

Text generation
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> **Ready-to-run Vision Language Model** — bundled Q4\_K\_M quantization + multimodal projector for immediate VLM inference with llama.cpp. This repository provides a **complete, self-contained VLM package** for Qwen3.5-9B in GGUF format. Unlike text-only GGUF repos, this includes the **multimodal projector (mmproj)** required for vision capabilities — no need to hunt for separate files. Qwen3.5 represents a paradigm shift from the separate "LLM + Vision Encoder" approach (used by Qwen3-VL, LLaVA, etc.) to a **unified early-fusion architecture**:

Visual QA
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**W4A16 GPTQ quantization** of Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU using AutoRound with GPTQ group size 128. **Key findings:** - W4A16 **matches or exceeds** FP16 on all classification/generation benchmarks - Perplexity difference is negligible (0.13%) — well within measurement variance - No perceivable quality loss at 4-bit weights with 16-bit activations See the FP16 original model card for full details on training methodology, stages, and capabilities.

Visual QA
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