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AI モデル 84759

• chronos-13B-GGUF↗

  TheBloke

• Falcon-E-1B-Instruct-GGUF↗

  tiiuae

• Qwen3-30B-A3B-Base↗

  unsloth

• t5-small-qa-qg-hl↗

  valhalla

• BindWeave-GGUF↗

  vantagewithai

• NVIDIA-Nemotron-Nano-12B-v2-VL-BF16-GGUF↗

  Vastined

• gemma-4-E4B-it-W4A16-AutoRound-GPTQ↗

  Vishva007

  This repository hosts **W4A16 INT4-quantized** versions of `google/gemma-4-E4B-it`, a multimodal mixture-of-experts model supporting text, vision, and audio inputs. Two quantized variants are available: > **Note on MTP / Speculative Decoding:** > If you want to use the official speculative decoding assistant model (`google/gemma-4-E4B-it-assistant`) for MTP support, it is recommended to use the `vllm/vllm-openai:gemma4-0505-cu129` Docker image, which includes newer Gemma 4 support and decoding patches. > > Due to INT4 quantization, the assistant acceptance rate may be lower compared to the original unquantized `google/gemma-4-E4B-it` model. > **Note:** Only the language model (LM) layers are quantized to INT4. The vision tower, audio tower, and multimodal projectors are kept at full precision (BF16) to preserve multimodal quality.

• Qwen3.6-35B-A3B-DSPARK-GGUF↗

  williamliao

• Zamba2-2.7B↗

  Zyphra

  Zamba2-2.7B is a hybrid model composed of state-space and transformer blocks. It broadly follows the Zamba architecture which consists of a Mamba backbone alternating with shared transformer blocks (see diagram in Model Details). Zamba2-2.7B possesses three major improvements over Zamba1: 2.) Instead of a single shared attention block, we utilize two shared attention blocks which are interleaved in an ABAB pattern throughout the network. 3.) We apply a LoRA projector to each shared MLP block, which allows the network to specialize the MLPs at each invocation of the shared layer across depth. LoRA enables us to add depth-specialization for only a minimal increase in total parameter count.

• Ornith1.5-9B-3MPER0RR-abliterated↗

  3MPER0RR