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AI Models 84759

• CSD-ViT-L↗

  tomg-group-umd

• Mistral-Small-3.2-24B-Instruct-2506↗

  unsloth

  Small-3.2 improves in the following categories: - **Instruction following**: Small-3.2 is better at following precise instructions - **Repetition errors**: Small-3.2 produces less infinite generations or repetitive answers - **Function calling**: Small-3.2's function calling template is more robust (see here and examples) In all other categories Small-3.2 should match or slightly improve compared to Mistral-Small-3.1-24B-Instruct-2503. We compare Mistral-Small-3.2-24B to Mistral-Small-3.1-24B-Instruct-2503. For more comparison against other models of similar size, please check Mistral-Small-3.1's Benchmarks'

• Qwen3-0.6B-bnb-4bit↗

  unsloth

• EuroLLM-9B-2512↗

  utter-project

• Sakura-MiMo-V2.6-Flash-MOPD-P160-GGUF↗

  webmp3

• Eklav-8B-Math-CotGen↗

  AdarshSingh7647

• PatentSBERTa↗

  AI-Growth-Lab

  This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

• GLM-4.5-Air-iq4nl-GGUF↗

  auRose94

• chronos-bolt-mini↗

  autogluon

  🚀 **Update Feb 14, 2025**: Chronos-Bolt models are now available on Amazon SageMaker JumpStart! Check out the tutorial notebook to learn how to deploy Chronos endpoints for production use in a few lines of code. Chronos-Bolt is a family of pretrained time series forecasting models which can be used for zero-shot forecasting. It is based on the T5 encoder-decoder architecture and has been trained on nearly 100 billion time series observations. It chunks the historical time series context into patches of multiple observations, which are then input into the encoder. The decoder then uses these representations to directly generate quantile forecasts across multiple future steps—a method known as direct multi-step forecasting. Chronos-Bolt models are **more accurate**, up to **250 times faster** and **20 times more memory-efficient** than the original Chronos models of the same size. The following plot compares the inference time of Chronos-Bolt against the original Chronos models for forecasting 1024 time series with a context length of 512 observations and a prediction horizon of 64 steps.

• mlabonne_gemma-3-12b-it-abliterated-GGUF↗

  bartowski

  Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to. If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: You can either specify a new local-dir (mlabonne_gemma-3-12b-it-abliterated-Q8_0) or download them all in place (./)