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AI 模型 84753

• yolo↗

  Synaptics

  This is a copy of Yolo as distributed at https://ultralytics.com under the license described in LICENSE.

• sheep-duck-llama-2-13B-GGUF↗

  TheBloke

• yamnet↗

  thelou1s

• Swift-1.5-Qwen3.8-Flash-Next-DACAN↗

  tirex2001

• gemma-3-270m-it-qat↗

  unsloth

• gemma-4-31B-it-GGUF↗

  unsloth

  Jun 9 Update: Added MTP support. See our MTP Guide . Apr 11 Update: Re-download for Google's latest chat template and llama.cpp fixes. Gemma 4 can now be run and fine-tuned in Unsloth Studio . Read our guide . See all versions of Gemma 4 (GGUF, 16-bit etc.) in our collection . Example of Gemma 4 E4B (4-bit GGUF) running in Unsloth Studio with tool-calling: Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on small models) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages. Featuring both Dense and Mixture-of-Experts (MoE) architectures, Gemma 4 is well-suited for tasks like text generation, coding, and reasoning. The models are available in four distinct sizes: **E2B**, **E4B**, **26B A4B**, and **31B**. Their diverse sizes make them deployable in environments ranging from high-end phones to laptops and servers, democratizing access to state-of-the-art AI.

• 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`).