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AI 模型 84759

• Qwen3.5-9B-MTPLX-Optimized-Speed-FP16↗

  Youssofal

• Qwen3.5-4B-DFlash↗

  z-lab

  This DFlash draft model is a joint retrain from Z-Lab and Modal, trained with 40k sequence length and sliding-window attention for improved long-context performance. It is mirrored across the following Hugging Face repositories: This repository contains a DFlash draft model for `Qwen/Qwen3.5-4B`. It is not a standalone language model. It is intended to be paired with the target model in a speculative decoding server. DFlash uses a lightweight block diffusion draft model to propose multiple tokens in parallel. The target model verifies those proposals, improving serving throughput while preserving the target model's output distribution.

• cxza4↗

  zeras141a

• ea1553ba↗

  zeras141a

• dwight-4b↗

  zime-ai

• Qwen3.8-27B-Heretic-GSQ-RCO-GGUF↗

  0bserverx

  **Non-uniform GGUF quantizations** of **RVN Qwen3.8-27B-Heretic-Abliterated-Uncensored** (refusals 0-1/100, KL ~ 0.0085 vs base). Instead of one uniform quantization type for all tensors, **each of the 851 tensors carries its own type**, following the per-tensor **RCO allocation** published by IST-DASLab for their GSQ-RCO release of Qwen3.8-27B, quantized locally with **llama.cpp** and the **official importance matrix** `imatrix-qwen3.8-27b.gguf`. Files are standard GGUF and run unmodified in llama.cpp / Ollama / LM Studio. This family was made at the request of community member **Heidagger44** in discussion #11: > Regarding IQ3_S there is new method: GSQ-RCO, if it's possible to combine your abliteration method with this quantization, we would have a true refusal free Qwen3.8-27B running fully on 16GB cards.

• unifiedqa-v2-t5-base-1251000↗

  allenai

• flux-koda↗

  alvdansen

• ocr-captcha-v2↗

  anuashok

  This model is a fine-tuned version of microsoft/trocr-base-printed on your custom dataset. captchas like processor = TrOCRProcessor.from_pretrained("anuashok/ocr-captcha-v2") model = VisionEncoderDecoderModel.from_pretrained("anuashok/ocr-captcha-v2") generated_ids = model.generate(pixel_values) generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] print(generated_text)

• hkust-nlp_dsv2-lite-coder_codeio_pp-GGUF↗

  bartowski