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Rohan Paul@rohanpaul_ai
57AI 编辑部评分，满分 100
2026-07-31 09:52· 1小时前
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AI 摘要

Thinking Machines 发布开源权重模型 Inkling-Small，总参数量 276B、每 token 激活 12B，仅为 Inkling 激活参数（41B）的三分之一。该模型在 HLE 上以 31.6% 超过更大的 Inkling（29.7%），SWE-bench Verified 达 80.2%，并针对音频智能优化了预训练混合、知识蒸馏及两周智能体编码 RL。

Thinking Machines released open-weight model Inkling-Small， cuts Inkling's active parameter count from 41B to 12B per token.

Pushed ahead of the larger Inkling on HLE， 31.6% versus 29.7%， and to 80.2% on SWEBench Verified， while activating less than one-third as many parameters.

The model also received a revised pretraining mix， distillation from Inkling， and two additional weeks of agentic coding RL.

They said that they crafted Inkling-Small for audio intelligence， making it a good candidate for real-world audio applications.

Thinking MachinesToday, we are releasing Inkling-Small. Inkling-Small achieves comparable performance to Inkling at a quarter of its size. It features 276B total parameters, 12B...
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Rohan Paul · @rohanpaul_ai · X·2026-07-31 09:52·1小时前
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AI 摘要

Thinking Machines 发布开源权重模型 Inkling-Small，总参数量 276B、每 token 激活 12B，仅为 Inkling 激活参数（41B）的三分之一。该模型在 HLE 上以 31.6% 超过更大的 Inkling（29.7%），SWE-bench Verified 达 80.2%，并针对音频智能优化了预训练混合、知识蒸馏及两周智能体编码 RL。

Thinking Machines released open-weight model Inkling-Small， cuts Inkling's active parameter count from 41B to 12B per token.

Pushed ahead of the larger Inkling on HLE， 31.6% versus 29.7%， and to 80.2% on SWEBench Verified， while activating less than one-third as many parameters.

The model also received a revised pretraining mix， distillation from Inkling， and two additional weeks of agentic coding RL.

They said that they crafted Inkling-Small for audio intelligence， making it a good candidate for real-world audio applications.

Thinking MachinesToday, we are releasing Inkling-Small. Inkling-Small achieves comparable performance to Inkling at a quarter of its size. It features 276B total parameters, 12B...
开源/仓库模型发布
在 X 查看原推x.com