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AI 모델 84759

• Llama-3.1-CyberAlpha8B↗

  Rumiii

• Sailor2-1B↗

  sail

• sot-phase2-wikitext103-heuristic-seed42-tc768d013-v8-tailfreeze↗

  sbartlett97

• GLM-5.3-Flash-UNCENSORED-mlx-oQ4e-DFlash2↗

  Solstice-AI

  Official Solstice-AI Apple Silicon Release • Native Multimodal Vision + Video • 1M Context Window (1,048,576 Tokens) • Bundled DFlash 2 Speculative Drafter Original Architecture by Zhipu AI / ZAI • Uncensored Weights by dealignai • oQ4e Mixed-Precision by Solstice-AI **`Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ4e`** is the official **oQ4e** mixed-precision release of the uncensored 320B foundation model, **GLM-5.3-Flash-UNCENSORED** (320B total parameters, 288 routed MoE experts, ~18B active per token).

• japanese-stablelm-instruct-alpha-7b↗

  Stability AI

• Apertus-v1.1-1.5B↗

  swiss-ai

• community-forensics-frontier-detector-2026-08↗

  Thermostatic

• convnext_small.fb_in22k_ft_in1k_384↗

  timm

  A ConvNeXt image classification model. Pretrained on ImageNet-22k and fine-tuned on ImageNet-1k by paper authors. - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 50.2 - GMACs: 25.6 - Activations (M): 63.4 - Image size: 384 x 384 - **Papers:** - A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545 - **Original:** https://github.com/facebookresearch/ConvNeXt - **Dataset:** ImageNet-1k - **Pretrain Dataset:** ImageNet-22k Explore the dataset and runtime metrics of this model in timm model results.

• olmOCR-2-7B-1025-FP8↗

  TitanML

• DeepSeek-Prover-V2-7B-GGUF↗

  unsloth

  We introduce DeepSeek-Prover-V2, an open-source large language model designed for formal theorem proving in Lean 4, with initialization data collected through a recursive theorem proving pipeline powered by DeepSeek-V3. The cold-start training procedure begins by prompting DeepSeek-V3 to decompose complex problems into a series of subgoals. The proofs of resolved subgoals are synthesized into a chain-of-thought process, combined with DeepSeek-V3's step-by-step reasoning, to create an initial cold start for reinforcement learning. This process enables us to integrate both informal and formal mathematical reasoning into a unified model. we introduce ProverBench, a benchmark dataset comprising 325 problems. Of these, 15 are formalized from number theory and algebra questions featured in the recent AIME competitions (AIME 24 and 25), offering authentic high-school competition-level challenges. The remaining 310 problems are drawn from curated textbook examples and educational tutorials, contributing a diverse and pedagogically grounded collection of formalized mathematical problems. This benchmark is designed to enable more comprehensive evaluation across both high-school competition problems and undergraduate-level mathematics. We release DeepSeek-Prover-V2 in two model sizes: 7B and 671B parameters. DeepSeek-Prover-V2-671B is trained on top of DeepSeek-V3-Base. DeepSeek-Prover-V2-7B is built upon DeepSeek-Prover-V1.5-Base and features an extended context length of up to 32K tokens.