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AI Models 84759

• Qwen3.8-27B-lecore-mlx↗

  staccs

• tts-vits-lug↗

  Sunbird

• TinyLlama-1.1B-Chat-v1.0-AWQ↗

  TheBloke

• eva02_large_patch14_448.mim_m38m_ft_in22k_in1k↗

  timm

  An EVA02 image classification model. Pretrained on Merged-38M (IN-22K, CC12M, CC3M, COCO (train), ADE20K (train), Object365, and OpenImages) with masked image modeling (using EVA-CLIP as a MIM teacher) and fine-tuned on ImageNet-22k then on ImageNet-1k by paper authors. EVA-02 models are vision transformers with mean pooling, SwiGLU, Rotary Position Embeddings (ROPE), and extra LN in MLP (for Base & Large). NOTE: `timm` checkpoints are float32 for consistency with other models. Original checkpoints are float16 or bfloat16 in some cases, see originals if that's preferred.

• hardcorenas_a.miil_green_in1k↗

  timm

• vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k↗

  timm

• granite-4.1-3b↗

  unsloth

• Hermes-3-Llama-3.1-8B↗

  unsloth

• Llama-3.2-11B-Vision-Instruct-bnb-4bit↗

  unsloth

  We have a free Google Colab Tesla T4 notebook for Llama 3.2 Vision (11B) here: https://colab.research.google.com/drive/1j0N4XTY1zXXy7mPAhOC1_gMYZ2F2EBlk?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. The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks.

• Qwen2.5-1.5B-unsloth-bnb-4bit↗

  unsloth

  Unsloth's Dynamic 4-bit Quants is selectively quantized, greatly improving accuracy over standard 4-bit. We have a free Google Colab Tesla T4 notebook for Qwen2.5 (7B) here: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_(7B)-Alpaca.ipynb 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.