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.

Text generation

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DeepSWE-Preview

agentica-org

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Text generation

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DeepSeek-V4.1-Flash-Skeleton-GGUF

agentionai

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Qwen3.8-27B-AP-GGUF

agentionai

Agention Precision quants are the highest precision quants of Qwen3.8 27B byte-for-byte. Each file has a non-uniform assignment and low-loss error correction built with a custom encoder. This is a drop-in GGUF pack for Qwen3.8-27B: standard llama.cpp types, no fork, no flags. Vision projector and MTP draft head included. Every 27B quant gives something up. These give up less. We measured the leading public GGUFs against the full BF16 model on one protocol. At every size we ship, ours is the closest to Qwen’s next-token distribution.

Text generationOpen weights

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