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AI モデル 84759

• Laguna-XS-2.1-GGUF↗

  AtomicChat

• Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-giant_loud_llama↗

  BabaYaga0001

• Palmyra-Med-70B-32K-GGUF↗

  bartowski

• mvpformer-s-pretrained↗

  braindecode

• Jev-Style-0.8B-Decision-v3-GGUF↗

  chaoliangUNSW

  **Local text classification, workflow routing, and fixed-choice scoring with JevStyle.** This 0.8B v3 release is the package’s default text decision model. It accepts text and JSON, not images; its PyTorch, MLX, and GGUF distributions require the matching decision runtime. Maintained by **Chaoliang Yan** (GitHub: `lawrence3699`), as an independent project, not affiliated with TypeSafe AI. Training provenance is specific to each release; see this card’s training section. The separate 2B v4 release discloses soft-label distillation from TypeSafe Jev 1.14 through its public API. Choose a workflow: local text classification in Python · choosing a local decision model · this model’s setup and limits · evaluation evidence.

• Qwen3.8-27B-Uncensored-OrcaRouter-GGUF↗

  chimingw

  This is an unofficial preservation, conversion, and quantization release of `orcarouter/Qwen3.8-27B-Uncensored-FP8` at pinned revision `21411e351948ec029617fa3c9833adcb2ad25da9`. One public repository contains the byte-identical FP8 mirror, the FP8-derived BF16 GGUF and matching BF16 multimodal projector, all four independently produced llama.cpp quant choices, and an optional OrcaRouter-derived MTP-only speculative-draft companion. No training, fine-tuning, merging, or alignment change was performed for this release or its MTP companion. > **Important precision note:** the BF16 conversion expands the numerical values represented by the released FP8 weights and their scales into BF16 storage. It does **not** recover the unavailable pre-FP8 weights or reverse information lost during the source model's FP8 quantization.

• minimax-h3-turbo-GGUF↗

  ChrisColeTech

  The MiniMax H3 **FL2VA (pruned)** DiT with the **LightX2V Turbo 8-step v1.0** LoRA **baked in**. The MiniMax H3 **REF2VA (pruned)** DiT with the **LightX2V Turbo 4-step v1.0** LoRA **baked in**. Both samples use the **same** turbo **Q4_K_M turbo GGUF** diffusion model, prompt, and settings, only the **text encoder** changes (4B vs 8B).

• Wan2.1-Fun-14B-InP-gguf↗

  city96

• audioseal-GGUF↗

  cstr

• Spaetzle-v60-7b↗

  cstr