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AI Models 84997

• swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-Lesion-Classification-HAM10000-AH↗

  ALM-AHME

• apertus-v1.5-8b-text-Q8_0-GGUF↗

  andreasmartin

• BanglaTag↗

  arafatfahim

• Gemma-4-Writers-31B↗

  Ateron

• embeddinggemma-2-GGUF↗

  AtomicChat

• Qwen3.8-27B-INT8-W8A8↗

  Avesed

  INT8 weight **and** activation (**W8A8**) quantization of **Qwen/Qwen3.8-27B** — a dense hybrid model (GatedDeltaNet linear-attention + periodic full-attention) with a native vision tower and an MTP head. ~30 GB, so it needs two 24 GB cards (tensor-parallel 2) but leaves a comfortable KV cache. Pick this over the INT4-W4A16 build when you want the fastest prefill and have the VRAM; pick INT4 when you want the fastest decode or a single-card fit. The **MTP head is included** (`mtp.safetensors`, bf16) and is listed in `quantization_config.ignore`, so vLLM loads it unquantized and speculative decoding works out of the box. Quantizers silently drop this head — if you re-quantize the base yourself, graft it back or spec-decode will accept 0 %.

• evalexplorer-classify-gguf↗

  baobabtech

• NemoMix-Unleashed-12B-GGUF↗

  bartowski

  All quants made using imatrix option with dataset from here 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. Some say that this improves the quality, others don't notice any difference. If you use these models PLEASE COMMENT with your findings. I would like feedback that these are actually used and useful so I don't keep uploading quants no one is using.

• beetle-bilingual-balanced-b1-fineweb-2b-fin-eng↗

  Beetle-FineWeb-24B-5

• reina↗

  cocktailpeanut