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AI 모델 85003

• mistral-7b-instruct-v0.3↗

  unsloth

  We have a Google Colab Tesla T4 notebook for Mistral v3 7b here: https://colab.research.google.com/drive/1_yNCks4BTD5zOnjozppphh5GzMFaMKq_?usp=sharing For conversational ShareGPT style and using Mistral v3 Instruct: https://colab.research.google.com/drive/15F1xyn8497_dUbxZP4zWmPZ3PJx1Oymv?usp=sharing 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.

• nli-deberta-v3-small↗

  Xenova

• Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sprightly_gregarious_barracuda↗

  Zeniang

• Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-YMQ-GGUF↗

  zerodigest

• Unsloth-Qwen3.8-27B-UD-KQVQ4-IQ3Floor-10G-MTP-GGUF↗

  zhangrover

• MolGen-large↗

  zjunlp

• Yi-6B-Chat↗

  01-ai

  - they might want nothing more than destruction itself rather then anything else from their quest after immortality (and maybe someone should tell them about modern medicine)? In any event though – one thing remains true regardless : whether or not success comes easy depends entirely upon how much effort we put into conquering whatever challenges lie ahead along with having faith deep down inside ourselves too ;) So let’s get started now shall We?" pipeline_tag: text-generation --- - What is Yi? - Introduction - Models - Chat models - Base models - Model info - News - How to use Yi? - Quick start - Choose your path - pip - docker - llama.cpp - conda-lock - Web demo - Fine-tuning - Quantization - Deployment - FAQ - Learning hub - Why Yi? - Ecosystem - Upstream - Downstream - Serving - Quantization - Fine-tuning - API - Benchmarks - Base model performance - Chat model performance - Tech report - Citation - Who can use Yi? - Misc. - Acknowledgements - Disclaimer - License - 🤖 The Yi series models are the next generation of open-source large language models trained from scratch by 01.AI.

• BArtstyle-Blue-Archive-Artstyle-LoRA-for-Krea2↗

  4BEraser

• indicwav2vec-hindi↗

  ai4bharat

• qwen2.5-vl-32b-instruct↗

  阿里巴巴 · Qwen

  In addition to the original formula, we have further enhanced Qwen2.5-VL-32B's mathematical and problem-solving abilities through reinforcement learning. This has also significantly improved the model's subjective user experience, with response styles adjusted to better align with human preferences. Particularly for objective queries such as mathematics, logical reasoning, and knowledge-based Q&A, the level of detail in responses and the clarity of formatting have been noticeably enhanced. In the past five months since Qwen2-VL’s release, numerous developers have built new models on the Qwen2-VL vision-language models, providing us with valuable feedback. During this period, we focused on building more useful vision-language models. Today, we are excited to introduce the latest addition to the Qwen family: Qwen2.5-VL. * **Understand things visually**: Qwen2.5-VL is not only proficient in recognizing common objects such as flowers, birds, fish, and insects, but it is highly capable of analyzing texts, charts, icons, graphics, and layouts within images.