全站搜索
AI 模型 85001
- 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
- Qwen3.8-27B-oQ2↗
TensorFold
- claude2-alpaca-13B-GGUF↗
TheBloke
- UnslopNemo-12B-v4-GGUF↗
TheDrummer
- efficientformerv2_s0.snap_dist_in1k↗
timm
- Mina-1B-Vietnamese-Master↗
Trivnpk
- DeepSeek-R1-BF16↗
unsloth
You can view more detailed instructions in our blog: unsloth.ai/blog/deepseek-r1 1. Do not forget about ` ` and ` ` tokens! - Or use a chat template formatter 2. Obtain the latest `llama.cpp` at https://github.com/ggerganov/llama.cpp 3. Example with Q8_0 K quantized cache **Notice -no-cnv disables auto conversation mode** 4. If you have a GPU (RTX 4090 for example) with 24GB, you can offload multiple layers to the GPU for faster processing. If you have multiple GPUs, you can probably offload more layers. We have a free Google Colab Tesla T4 notebook for Llama 3.1 (8B) here: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-Alpaca.ipynb
- Qwen2.5-32B-Instruct-bnb-4bit↗
unsloth
We have a Qwen 2.5 (all model sizes) free Google Colab Tesla T4 notebook. Also a Qwen 2.5 conversational style notebook. All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face. - This conversational notebook is useful for ShareGPT ChatML / Vicuna templates. - This text completion notebook is for raw text. This DPO notebook replicates Zephyr. - \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.
- EuroMoE-2.6B-A0.6B-Instruct-2512↗
utter-project