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AI モデル 84759

• byt5-large↗

  Google

  ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream task. ByT5 works especially well on noisy text data,*e.g.*, `google/byt5-large` significantly outperforms mt5-large on TweetQA. Authors: *Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel*

• multiberts-seed_3-step_1800k↗

  Google

• rag-intrinsics-lib↗

  ibm-granite

• multilingual-e5-base↗

  intfloat

  Multilingual E5 Text Embeddings: A Technical Report. Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024 This model is initialized from xlm-roberta-base and continually trained on a mixture of multilingual datasets. It supports 100 languages from xlm-roberta, but low-resource languages may see performance degradation. Here are some rules of thumb: - Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval.

• Ornith-1.5-122B-DFlash2-GGUF↗

  kaivoss

  This model was converted to GGUF format from `incoai/Ornith-1.5-122B-DFlash2` using upstream llama.cpp (`ead9f32`). This is a **DFlash 2 draft model** (0.9B, block-diffusion drafter) for `ornith-ai/Ornith-1.5-122B`. It is not a standalone model. It reuses the target's token embeddings and LM head at runtime, so load it with `-md` next to an Ornith-1.5-122B GGUF. The following quants are available: BF16, F16, F32, IQ3_M, IQ3_S, IQ4_NL, IQ4_XS, MXFP4_MOE, Q1_0, Q2_0, Q2_K, Q3_K, Q3_K_L, Q3_K_M, Q3_K_S, Q4_0, Q4_1, Q4_K, Q4_K_M, Q4_K_S, Q5_0, Q5_1, Q5_K, Q5_K_M, Q5_K_S, Q6_K, Q8_0, TQ1_0, TQ2_0

• HunyuanVideo_comfy↗

  Kijai

  To be used with ComfyUI native HunyuanVideo implementation, or my wrapper: https://github.com/kijai/ComfyUI-HunyuanVideoWrapper GGUF's created using city96's scripts, and only works with their nodes: https://github.com/city96/ComfyUI-GGUF

• Qwen3.8-Flash-Next-GSQ-RCO-abliterated-GGUF↗

  KilltheSniper

  GSQ-RCO upstream · layout preserved 144 tensors transplanted IQ3_S / IQ3_XXS / IQ2_XS / Q2_0 per-tensor blake2b verified 262K context APACHE-2.0 Qwen3.8-Flash-Next · GSQ-RCO-abliterated The refusal direction removed from an already-quantized GSQ-RCO model — 144 tensors across all 48 layers swapped for ready-made abliterated weights, with every value and scale GSQ learned left untouched and the upstream per-tensor type assignment restored exactly . The IQ3_S build is verified tensor by tensor: everything that changed was supposed to, nothing else moved. 🧭 What this repository is An abliterated build of the official ISTA-DASLab GSQ-RCO quantizations . Not a single one of GSQ's learned quantized tensors was recomputed — 144 "write-to-residual-stream" projection tensors across all 48 layers were replaced with the corresponding tensors from a ready-made abliterated release. Base ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF — GSQ learned quantization + RCO budget-constrained type assignment Ablation source orcarouter/Qwen3.8-Flash-Next-Uncensored-GGUF — the 144 target tensors are taken straight from it Method Byte-level GGUF → GGUF tensor transplant (135 of 144 targets); the 9 remaining expert tensors, which upstream stores as Q2_0, are re-encoded from the abliterated values using GSQ's own learned block scales — no GSQ value is recomputed, only the codes Not done Any dequantize → quantize cycle; any re-quantization from BF16 / safetensors In one line: what you get is still the GSQ-RCO quantization you already know , with those 144 tensors abliterated. It is the only abliteration route that preserves GSQ's work. ⚠️ Safety notice This model is abliterated and refusal-removed — it has no meaningful built-in guardrails and may comply with harmful, illegal or unsafe requests. Deploy it only where you can supply your own moderation, access control and legal review; do not put it in front of end users without a safety layer of your own. 本模型经过拒答方向消融，不具备可靠的内置安全护栏，可能配合有害、违法或不安全的请求。请仅在你能够提供审核、访问控制与法律审查的场景部署。

• LTX-Video-0.9.8-13B-distilled↗

  Lightricks

• depth-anything-v2-small↗

  litert-community

  LiteRT/TFLite exports of Depth Anything V2 Small, including the complete depth decoder. This model estimates a relative-depth map from a single RGB image. Its outputs are not calibrated distances in meters. The source has a DINOv2 Small backbone and a DPT decoder, with approximately 24.8 million parameters. These files are based on the official Transformers checkpoint at revision `5426e4f0f36572d16453bbda7a8389317b1bef99`. The INT8 variant uses AI Edge Quantizer's `dynamic_wi8_afp32()` recipe. Supported weight tensors are quantized; the model is not fully integer, and floating-point tensors remain. No calibration images are required. Both files have the same input and output contract.

• Yi-Coder-1.5B-Chat-GGUF↗

  lmstudio-community