全站搜索

AI 模型 84759

• sapiens-pose-1b-bfloat16↗

  facebook

• hin-deva-10mb-after-ppt-Dp-10mb-packed-bfdiso-ckpt500_seed455↗

  francesca9805

• gemma-4-26B-A4B-it-qat-q4_0-unquantized↗

  Google

  > [!Note] > This model card is for the new versions of the Gemma 4 family optimized with Quantization-Aware Training (QAT), which allows preserving similar quality to bfloat16 while dramatically reducing the memory requirements to load the model. > Four versions of the QAT checkpoints are available: > * **Unquantized QAT checkpoints** (Q4_0): Half-precision weights extracted from the QAT pipeline, ideal for custom downstream compilation and research. Available for Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B, and their drafter models. > * **GGUF** (Q4_0): Ready-to-deploy formats for broad ecosystem compatibility. Available for Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B. > * **Mobile-optimized** (wNa8o8): A custom schema engineered explicitly for mobile hardware efficiency. It features targeted 2-bit decoding layers, optimized KV caches, and static activations to maximize VRAM savings. Available for Gemma 4 E2B and E4B. > * **Compressed Tensors** (w4a16): QAT checkpoints serialized in the compressed-tensors format for native, optimized inference with vLLM. Available for Gemma 4 E2B, E4B, 12B, and 31B. > * **Assistant Compatibility**: When using multi-token prediction (speculative decoding) with an assistant model alongside a QAT target model, the assistant model must also be a QAT checkpoint with the same precision to ensure compatibility. 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 E2B, E4B, and 12B) 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 five distinct sizes: **E2B**, **E4B**, **12B**, **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.

• gemma-7b-quant-pytorch↗

  Google

• pix2struct-ocrvqa-large↗

  Google

• t5gemma-9b-9b-prefixlm-it↗

  Google

• smartsightCoach-archive↗

  hanemay

• long-seq-paraphrase-MiniLM-L6-v2↗

  harsh-wk

• Qwen3.5-35B-A3B-abliterated-GGUF↗

  HeYujie

  This repository contains the GGUF quantized versions of huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated is an uncensored version of Qwen/Qwen3.5-35B-A3B created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens.

• exaone-3.5-7.8b-emt-qlora-awq-v2↗

  hymmmm