サイト内検索
AI モデル 84999
- ndebuhr_-_Mistral-7B-Technical-Tutorial-Summarization-QLoRA-gguf↗
RichardErkhov
- onekq-ai_-_starcoder2-3b-instruct-v0.1-gguf↗
RichardErkhov
- krishidrishti-plant-disease↗
ripro805
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- ProseLens↗
rishanthrajendhran
- Kimi-K3-Uncensored-GGUF↗
Ryanchen911
- Qwen3-VL-8B-Instruct-heretic↗
saidutta69
- parrots-chinese-roberta-wwm-ext-large↗
shibing624
- stanza-da↗
stanfordnlp
- TLive-Omni-4B↗
TaoLiveAIGC
TLive-Omni is an omni-modal understanding model for e-commerce live-stream, mapping image, video, audio, and text into a unified text-output interface. Built on a Qwen3.5 backbone with a grafted AuT audio encoder, it supports up to 256K tokens of context, trained via a three-stage SFT recipe followed by Faithful-RFT reinforcement fine-tuning. - **Timestamped Per-vGrid layout** — Audio and video tokens are organized into timestamped grid with explicit boundaries, keeping audio segments adjacent to their corresponding visual content for fine-grained temporal alignment over long streams. - **Three-stage SFT recipe** — Progressive training from audio-language alignment to full multimodal SFT, developing live-commerce understanding from omni-modal perception to instruction-following responses. - **Faithful-RFT** — A reinforcement fine-tuning stage for faithful and real-time live-stream demands, suppressing explicit reasoning traces and directly optimizing answer quality for live-commerce tasks. - **Rich atomic capabilities** — A scenario-oriented taxonomy covering speech recognition, speaker analysis, product visual grounding, text recognition, temporal grounding, video dense caption, and omni-modal QA, etc, supported by a compact data production engine. - **Strong live-commerce performance with competitive generalization** — 4B and 9B variants demonstrate strong results across live-commerce audio, image, and video tasks, together with excellent generalization on general benchmarks. TLive-Omni is built on a Qwen3.5 backbone and extends it with a audio encoder through a lightweight MLP aligner, forming a unified text-output omni-modal understanding model. For video inputs with audio, each temporal grid is organized into a timestamped grid that interleaves video and audio token blocks, keeping audio segments adjacent to their corresponding visual content. The model supports up to 256K tokens of context at inference.
- wav2vec2-quran-phonetics↗
TBOGamer22
**Quranic Wav2Vec2 Phonetic ASR** is a fine-tuned `wav2vec2` model designed specifically for **phonetic transcription of Quranic recitation**. Unlike standard Arabic ASR systems that output orthographic Arabic text, this model outputs a **phonetic (sound-level) representation**, making it suitable for: - Tajweed research and education - Word-level pronunciation analysis - Forced alignment of Quranic recitation - Linguistic studies of Classical Arabic phonetics