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星期五

  1. Claude Platform：开发者版本说明精选

    Anthropic 发布 Claude Opus 4 与 Sonnet 4

    Anthropic 发布 Claude Opus 4 和 Claude Sonnet 4，在 SWE-bench 上分别取得 72.5% 和 72.7%。两款模型支持扩展思考时调用工具、并行工具执行和记忆文件，Claude Code 正式可用并新增 VS Code、JetBrains 扩展及 GitHub Actions 后台任务。

    Team, and Enterprise Claude plans include both models and extended thinking, with Sonnet 4 also available to free users. Both models are available on our API, Amazon Bedrock, and Google Cloud's Vertex AI. Pricing remains consistent with previous Opus and Sonnet models: Opus 4 at $15/$75 per million tokens (input/output) and Sonnet 4 at $3/$15. Claude 4 Claude Opus 4 is our most powerful model yet and the best coding model in the world, leading on SWE-bench (72.5%) and Terminal-bench (43.2%). It

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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。

    e Points’ pricing of $10/hour, comes to a total of $60,000. On the other hand, feeding 15 trillion tokens to popular decoder-only models, even with the lowest-cost option of using Google’s Gemini Flash and its low inference cost of $0.075/million tokens, would cost over one million dollars! Performance Overview Here’s a snapshot of the accuracy of ModernBERT and other models across a range of tasks, as measured by standard academic benchmarks – as you can see, ModernBERT is the only model which

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星期一

  1. Zyphra Research

    Zyphra 发布开源小模型 Zamba2-7B

    Zyphra 发布 7B 规模小语言模型 Zamba2-7B，采用 Mamba2 与两个共享注意力块交替的混合架构，并以 Apache 2.0 开源权重。官方称其在评测基准上超过 Mistral-7B、Gemma-7B 和 Llama3-8B，首 token 时间快 25%、每秒 token 数提升 20%，内存占用也明显降低。模型基于 3 万亿 token 预训练数据，并在 128 张 H100 上训练约 50 天。

    Zyphra is excited to release Zamba2-7B, a state-of-the-art small language model. At the 7B scale, we outperform the leading models of Mistral, Google’s Gemma and Meta’s Llama3 series in both quality and performance. Zamba2-7B achieves SOTA evaluation benchmark performance and superior inference efficiency compared to currently leading 7B models such as Mistral-7B, Gemma-7B, and Llama3-8B; Zamba2-7B is extremely inference-efficient, achieving 25% faster time to first token, a 20% improvement in t

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星期三

  1. Cartesia 产品与研究

    Cartesia 发布端侧智能更新：开源 Edge 库、1.3B 模型 Rene 与 Sonic On-Device

    Cartesia 宣布端侧智能首个里程碑，发布开源库 Edge、1.3B 参数 SSM 语言模型 Rene，以及支持低延迟实时流式生成的 Sonic On-Device 私有测试版。Rene 采用 Mamba-2 混合架构，在多项基准上表现与同级 SOTA 小模型相当，Edge 已支持 Apple MLX 和量化推理。

    The on-device intelligence update

    reasoning (COPA, WinoGrande, ARC-Easy, ARC-Challenge) and language understanding (PIQA, HellaSwag, OpenBookQA, MMLU). Rene also outperforms SLMs such as Apple’s OpenELM (1.1B) and Google’s recurrent Gemma (2B) (Figure 1). Rene is the first SLM based on a recurrent architecture to achieve similar performance to similarly-sized (≤2B parameters) state-of-the-art SLMs across these benchmarks. On-device. Edge supports Rene natively in MLX without any quality loss compared to its PyTorch implementatio

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星期二

  1. Zyphra Research

    Zyphra 发布 1.2B 小模型 Zamba2-mini，权重以 Apache 2.0 开源

    Zyphra 发布 1.2B 小语言模型 Zamba2-mini，称其达到 Llama2 7B 级别的性能，4bit 量化下内存占用不足 700MB，并以 Apache 2.0 协议开源模型权重。该模型采用 Mamba2 与共享注意力层交替的混合架构，相比 Phi1.5-1.3B 首 token 时间快 1.67 倍、内存开销降低 23.3%。模型在约 3T token 的 Zyda 及公开数据集上预训练，并在 100B 高质量 token 上退火，已提供 Hugging Face 集成与纯 PyTorch 实现。

    i (1.2B) Highlights Zamba2-mini achieves SOTA evaluation benchmark performance and superior inference efficiency compared to models of a similar scale and larger such as Gemma-2B (Google), SmolLM-1.7B (Huggingface), OpenELM-1.1B (Apple), StableLM-1.6B (StabilityAI) and Phi-1.5 (Microsoft); Zamba2-mini is extremely inference-efficient, achieving 1.67x faster time-to-first-token and a 23.3% reduction in memory overhead compared to Phi1.5-1.3B; We release the model weights open-source (Apache 2.0)

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    #lab#模型发布#开源生态#端侧

星期日

  1. Zyphra Research

    Zyphra 发布 2.7B 端侧模型 Zamba2-small

    Zyphra 发布 Zamba2-small，一个面向端侧应用的 2.7B 小语言模型，权重以 Apache 2.0 开源。该模型将 Zamba-7B 的 Mamba1 块替换为 Mamba2 块，并采用两个 ABAB 交错共享注意力块，对每个共享 MLP 块应用 LoRA projector。相比 Phi3-3.8B，其首 token 时间快 2 倍、内存开销降低 27%、生成延迟低 1.29 倍，并保持 3-4B 稠密 Transformer 的质量而只需 1-2B 的推理算力与内存。

    evice applications. Zamba2-small (2.7B) achieves SOTA evaluation benchmark performance and superior inference efficiency compared to models of a similar scale such as Gemma2-2.7B (Google), StableLM-3B (StabilityAI), OpenELM-3B (Apple) and Phi2-2.7B (Microsoft); Architectural improvements over Zamba-7B :Mamba1 blocks have been replaced with Mamba2 blocks; Instead of a single shared attention block, we utilize two shared attention blocks which are interleaved in an ABAB pattern throughout the netw

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    #lab#模型发布#端侧#开源生态