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星期二

  1. Mistral AI精选

    Mistral AI 发布 Voxtral 开源语音理解模型

    Mistral AI 发布 Voxtral 语音理解模型，包括面向生产应用的 24B 版本和面向本地及边缘部署的 3B 版本，均采用 Apache 2.0 许可证。模型支持 32k Token 上下文、最长 30 分钟转录或 40 分钟理解，并提供音频问答、摘要、多语言识别和语音 Function Calling。模型可从 Hugging Face 下载或通过 API 使用，价格为每分钟 $0.001 起。

    Triangle Voxtral Blog

    els ideal for real-world interactions and downstream actions, such as summaries, answers, analysis, and insights. For cost-sensitive use-cases, Voxtral Mini Transcribe outperforms OpenAI Whisper for less than half the price. For premium use cases, Voxtral Small matches the performance of ElevenLabs Scribe, also for less than half the price. To assess Voxtral’s transcription capabilities, we evaluate it on a range of English and multilingual benchmarks. For each task, we report the macro-average

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    #lab#模型发布#语音#多模态

星期四

  1. Liquid AI 模型与工程博客精选

    Liquid AI 发布 LFM2 系列端侧基础模型

    Liquid AI 发布 LFM2 系列端侧基础模型，开源 0.35B、0.7B、1.2B 三个稠密权重，可在 Liquid Playground、Hugging Face 和 OpenRouter 试用。官方称其采用乘法门控与短卷积的混合架构，共 16 个模块，CPU 上 decode 与 prefill 速度最高为 Qwen3 的 2 倍，训练效率较上一代提升 3 倍。模型在 10T token 上预训练，以 LFM1-7B 做知识蒸馏，上下文长度扩展至 32k，并支持 CPU、GPU 和 NPU 部署。

    ven popular benchmarks covering knowledge (5-shot MMLU, 0-shot GPQA), instruction following (IFEval, IFBench), mathematics (0-shot GSM8K, 5-shot MGSM), and multilingualism (5-shot OpenAI MMMLU, 5-shot MGSM again) with seven languages (Arabic, French, German, Spanish, Japanese, Korean, and Chinese). LFM2-1.2B performs competitively with Qwen3-1.7B, a model with a 47% bigger parameter count. LFM2-700M outperforms Gemma 3 1B IT, and our tiniest checkpoint, LFM2-350M, is competitive with Qwen3-0.6B

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    #company#模型发布#端侧#开源生态

星期二

  1. Prime Intellect精选

    Prime Intellect 发布 INTELLECT-2：首个全球分布式强化学习训练的 32B 参数模型

    Prime Intellect 发布 INTELLECT-2，称其为首个 32B 参数全球去中心化强化学习训练，任何人都可无许可贡献异构算力。该训练以 QwQ-32B 为基座模型，沿用 DeepSeek-R1 的 GRPO 与可验证奖励方法，并配套开源 prime-RL 异步分布式 RL 库、Shardcast 权重分发库和 TOPLOC 可验证推理方案。推理节点可用消费级 GPU，例如 4×RTX 3090 即可参与训练，训练数据已发布在 Hugging Face。

    INTELLECT-2: The First Globally Distributed Reinforcement Learning Training of a 32B Parameter Model

    aradigm offers, for the first time, the possibility for decentralized training to reach state-of-the-art performance. Paradigm Shift for Decentralized Training With the release of OpenAI's O1 and DeepSeek's R1, a second scaling paradigm beyond pre-training emerged last year—one that allows models to spend more time reasoning, optimized through reinforcement learning. In our previous release, we argued why we believe these reasoning models, trained via reinforcement learning, are even better suit

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    #company#模型发布#数据/训练#开源生态

星期四

  1. Answer.AI 官方研发博客

    Answer.AI 与 LightOn 发布 ModernBERT，为 BERT 提供替代方案

    Answer.AI 与 LightOn 发布 ModernBERT，一个编码器专用模型系列，提供 base（149M 参数）和 large（395M 参数）两个版本，可作为 BERT 类模型的直接替换。它把上下文长度从多数编码器的 512 提升到 8192 token，并首次在编码器训练数据中加入大量代码，在检索、自然语言理解和代码检索任务上整体优于主流模型，速度最高可达 DeBERTa 的 4 倍。模型将随 transformers v4.48.0 发布，在此之前需从 main 分支安装，官方建议搭配 Flash Attention 2 使用。

    ained so each token can look forwards and backwards (bi-directionally). They are built for this, and it makes them very efficient at what they do. Basically, a frontier model like OpenAI’s O1 is like a Ferrari SF-23. It’s an obvious triumph of engineering, designed to win races, and that’s why we talk about it. But it takes a special pit crew just to change the tires and you can’t buy one for yourself. In contrast, a BERT model is like a Honda Civic. It’s also an engineering triumph, but more su

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    #company#模型发布#开源生态#数据/训练