谷歌开源端侧模型家族 Gemma 总下载量破 10 亿次,派生超 10 万个变体
谷歌开源端侧模型家族Gemma下载量破10亿,衍生10万变体,生态爆发力惊人
IT之家 8 月 21 日消息,Google(谷歌)当地时间 20 日宣布,其开源端侧模型家族 Gemma 的总下载量已突破 10 亿次 ;开发者在过去两年间 发布了超过 10 万个 Gemma 模型变体 ,构成了一个被谷歌称为 "Gemmaverse" 的创新生态系统。 谷歌列举了多个 Gemma…
谷歌开源端侧模型家族Gemma下载量破10亿,衍生10万变体,生态爆发力惊人
IT之家 8 月 21 日消息,Google(谷歌)当地时间 20 日宣布,其开源端侧模型家族 Gemma 的总下载量已突破 10 亿次 ;开发者在过去两年间 发布了超过 10 万个 Gemma 模型变体 ,构成了一个被谷歌称为 "Gemmaverse" 的创新生态系统。 谷歌列举了多个 Gemma…
用微调MedGemma助力低资源地区成像系统维护,医疗AI落地新思路。
arXiv:2608.08896v1 Announce Type: new Abstract: Imaging device downtime is a major barrier to healthcare delivery in low- and middle-income countries …
研究揭示LLM自动评分中存在的第一语言偏见,基于LoRA微调的开源模型在托福作文跨提示评估中的表现实验。
arXiv:2607.14605v1 Announce Type: new Abstract: This study examines the cross-prompt generalization and first-language (L1) scoring effects of a LoRA-…
跨平台LLM推理服务器,原生支持Qwen、Gemma、Mistral、LLaMa等主流架构,一键Docker部署并集成HuggingFace与S3/GCS存储
Article URL: https://zml.ai/posts/llmd/ Comments URL: https://news.ycombinator.com/item?id=48829412 Points: 3 # Comments: 2
手把手教你用 "gemma-trainer" 在本地微调 Gemma 4 E2B 模型,快速上手大模型定制!
Remember back in May when I introduced the gemma-skills repository? It's been rewarding to see how many of you have used my previous post to streamlin…
纯C++实现Gemma 3推理,Metal加速,让本地大模型运行更轻快。
Article URL: https://github.com/ybubnov/metalchat Comments URL: https://news.ycombinator.com/item?id=48786298 Points: 3 # Comments: 1
看 Ornith-1.0 用“自脚手架”思路玩转智能体编码,底层可选 Apache 2.0 的 Gemma 4,许可省心又开源友好。
Ornith-1.0: Self-Scaffolding LLMs for Agentic Coding This is an interesting new open weights (MIT licensed) model, the first model release from DeepRe…
从Claude到开源模型:研究发现LLM内部存在类似人类心理结构的情绪向量,并揭示其因果影响行为。
arXiv:2606.26987v1 Announce Type: cross Abstract: Recent work identified emotion vectors in Claude Sonnet 4.5, which are internal representations that…
一行命令部署LLM API,Flama 2.0自带聊天界面,轻松搞定Gemma等模型服务。
Article URL: https://flama.dev/blog/serving_llms_with_flama_cli/ Comments URL: https://news.ycombinator.com/item?id=48671740 Points: 17 # Comments: 0
从乘法函数到生成文本,用 Zig 手搓 GPT-2 与 Gemma,硬核实战带你彻底吃透 LLM 底层。
Article URL: https://hamanlp.org/small-kernels.html Comments URL: https://news.ycombinator.com/item?id=48659158 Points: 1 # Comments: 1
真实伤口图像上评估医学视觉语言模型与通用聊天机器人,HuluMed、MedGemma、Gemma 3、ChatGPT Plus、Claude Pro谁更强?
arXiv:2606.20723v1 Announce Type: new Abstract: Chronic wound assessment remains a clinically challenging task that requires accurate interpretation o…
盘点12款主流开源大模型,覆盖Meta、阿里、谷歌等头部玩家,一文掌握选型要点。
Twelve models worth knowing in 2026, each with one standout strength.
一行命令部署任何LLM,Flama让Gemma 4等模型在Apple Silicon上极速运行
Flama 2.0 brings first-class support for generative AI: downloading, packaging, and serving large language models (LLMs) is now as simple as running a…
Gemma 4赋能《Solstice Eternal》,用AI生成创意与强化叙事,打造沉浸式冒险体验。
This is a submission for the Gemma 4 Challenge: Build with Gemma 4 What I Built Solstice Eternal – AI Adventure Game Solstice Eternal is an interactiv…
本地模型性能跃升:M2 Mac实测达到前沿模型75%效果,低成本也能玩转Agentic Coding
Article URL: https://vickiboykis.com/2026/06/15/running-local-models-is-good-now/ Comments URL: https://news.ycombinator.com/item?id=48555993 Points: …
编辑单个神经元,就能修复大模型在长枚举任务中的重复循环崩溃?这项研究揭示了 Gemma 4 模型的深层问题与解法。
arXiv:2606.13705v1 Announce Type: cross Abstract: Yes. Can it cure doom loops? Probably not. The Gemma 4 instruction-tuned models share a reproducible…
基于文本扩散的开放AI模型,本地推理速度提升4倍,高效处理文本生成任务
IT之家 6 月 11 日消息,谷歌今天(6 月 11 日)发布公告,宣布推出 DiffusionGemma,是基于文本扩散机制的开放 AI 模型, 相比较自回归模型在本地推理速度上提升了 4 倍。 IT之家注:自回归模型(Autoregressive Model)是当前主流的大语言模型架构(如 G…
用Ollama配合QAT量化,10GB显存笔记本也能跑12B的Gemma 4大模型,内存仅需6.7GB。
This stack uses Ollama with Gemma 4 QAT to run a 12B model on a 10GB VRAM laptop GPU. The latest Gemma 4 QAT checkpoints reduce memory usage and enabl…
本地运行Gemma4实现人-LLM-Web交互,Bonsai项目让AI操作浏览器门槛大降
Article URL: https://drive.google.com/drive/folders/1QsdOvsBKIavXDhwikouzQMOc8H_94bQd Comments URL: https://news.ycombinator.com/item?id=48461245 Poin…
从Qwen换到Gemma 4 E4B:本地大模型的新选择,性能与易用性如何?
Article URL: https://digg.com/ai/bfr4bqhh Comments URL: https://news.ycombinator.com/item?id=48437537 Points: 2 # Comments: 0