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AI Models 84997

  • Llama-3.2-3B-Instruct-GGUF↗

    unsloth

    We have a free Google Colab Tesla T4 notebook for Llama 3.2 (3B) here: https://colab.research.google.com/drive/1T5-zKWM_5OD21QHwXHiV9ixTRR7k3iB9?usp=sharing For more details on the model, please go to Meta's original model card 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.

  • wav2vec2-xls-r-300m-khmer↗

    vitouphy

  • sweep-next-edit-1.5B↗

    Xenova

    A 1.5B parameter model for next-edit autocomplete, converted to ONNX format (as well as fp32 HF transformers-compatible version). Sweep Next-Edit predicts your next code edit before you make it. It runs locally on your laptop in under 500ms (with speculative decoding) and outperforms models over 4x its size on next-edit benchmarks. More details here. - **Parameters**: 1.5B - **Context Length**: 8192 tokens - **Base Model**: Qwen2.5-Coder

  • whisper-base.en↗

    Xenova

    https://huggingface.co/openai/whisper-base.en with ONNX weights to be compatible with Transformers.js. If you haven't already, you can install the Transformers.js JavaScript library from NPM using: Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).

  • zerank-1-small-reranker↗

    zeroentropy

    This 1.7B reranker is the smaller version of our flagship model zeroentropy/zerank-1. Though the model is over 2x smaller, it maintains nearly the same standard of performance, continuing to outperform other popular rerankers, and displaying massive accuracy gains over traditional vector search. We release this model under the open-source Apache License 2.0, in order to support the open-source community and push the frontier of what's possible with open-source models. NDCG@10 scores between `zerank-1-small` and competing closed-source proprietary rerankers. Since we are evaluating rerankers, OpenAI's `text-embedding-3-small` is used as an initial retriever for the Top 100 candidate documents.

  • Qwen2.5-72B-Instruct-abliterated-v2-GGUF↗

    zetasepic

  • zonformer↗

    zParik