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AI 모델 85003

• Noromaid-20B-v0.1.1-GGUF↗

  TheBloke

• dla102.in1k↗

  timm

• repvit_m3.dist_in1k↗

  timm

• Ornith-1.5-35B-A3B-NVFP4-512K-GGUF↗

  TizzyT566

• tiny-SmolVLMForConditionalGeneration↗

  trl-internal-testing

• flux-3-image-klein-base↗

  unsloth

• Qwen3.8-Flash-Next-W4A16↗

  VnimanieAI

• MN-Helcyon-GPT-4o-12b-v4.5-GGUF↗

  XeyonAI

• Qwen3.8-27B-ARA-abliterated-NVFP4-MTP↗

  aday777

  This is a multimodal, refusal-ablated (commonly described as "uncensored") W4A4 NVFP4 derivative of the reproducible `trohrbaugh/Qwen3.8-27B-heretic-ara` BF16 checkpoint. It is intended for native Blackwell NVFP4 inference. This artifact passed its text-capability, image-vision, benign refusal-surface, native MTP-acceptance, integrity, and clean-load gates on vLLM 0.23. See `BUILD_MANIFEST.json`, `VALIDATION_REPORT.json`, and `SHA256SUMS` for exact provenance and results. Video tensors/processors are preserved, but video input was not part of the live runtime gate. The live gate used `Qwen3_5ForConditionalGeneration`, native three-token MTP, the FlashInfer CUTLASS NVFP4 kernel, an 8,192-token context, and an RTX PRO 6000 Blackwell GPU. Loaded model memory was approximately 19.53 GiB.

• DeepScaleR-1.5B-Preview↗

  agentica-org

  DeepScaleR-1.5B-Preview is a language model fine-tuned from DeepSeek-R1-Distilled-Qwen-1.5B using distributed reinforcement learning (RL) to scale up to long context lengths. The model achieves 43.1% Pass@1 accuracy on AIME 2024, representing a 15% improvement over the base model (28.8%) and surpassing OpenAI's O1-Preview performance with just 1.5B parameters. Our training dataset consists of approximately 40,000 unique problem-answer pairs compiled from: - AIME problems (1984-2023) - AMC problems (prior to 2023) - Omni-MATH dataset - Still dataset We employ Deepseek's Group Relative Policy Optimization (GRPO), a simplified RL algorithm that extends PPO by: - Normalizing advantage function over all samples generated from the same prompt. - Applying KL divergence regularization on top of PPO's surrogate loss to prevent significant policy drift.