#olmo3 - Twitter Hashtag

10 months ago

إطلاق نموذج Olmo 3 ا��جديد، والمميز فيه إنه "مفتوح بالكامل" (Fully Open) . يعني الأكواد، البيانات، وطريقة التدريب كلها متاحة للكل، مو صندوق أسود مثل غيره. خيار رهيب للمطورين اللي يبون حرية كاملة وبدون تعقيدات في البنية التحتية. 🛠️ #Olmo3 #OpenSource #AI #تقنية

NovaRiseAI's tweet photo. إطلاق نموذج Olmo 3 ا��جديد، والمميز فيه إنه "مفتوح بالكامل" (Fully Open) .
يعني الأكواد، البيانات، وطريقة التدريب كلها متاحة للكل، مو صندوق أسود مثل غيره.
خيار رهيب للمطورين اللي يبون حرية كاملة وبدون تعقيدات في البنية التحتية. 🛠️
#Olmo3 #OpenSource #AI #تقنية https://t.co/MJEe9IHapT

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𝔸��𝕝𝕒𝕤 𝕀𝕟𝕥𝕖𝕝𝕚𝕘𝕖𝕟𝕥𝕖𝕤

@AulasInteligent

11 months ago

Hay un nuevo #Olmo3 y es un razonador realmente potente, gratuito, al que se le pueden vincular herramientas y totalmente open source. Lo hemos estado probando y va genial. Al final nos excedimos con un problema de razonamiento complejo y ha acabado entrando en un bucle...😅🤯

Ai2 @allen_ai

11 months ago

Announcing Olmo 3, a leading fully open LM suite built for reasoning, chat, & tool use, and an open model flow—not just the final weights, but the entire training journey. Best fully open 32B reasoning model & best 32B base model. 🧵

allen_ai's tweet photo. Announcing Olmo 3, a leading fully open LM suite built for reasoning, chat, & tool use, and an open model flow—not just the final weights, but the entire training journey.
Best fully open 32B reasoning model & best 32B base model. 🧵 https://t.co/vnGrArA44X

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Tim Janik @TimJanik

11 months ago

🎉 We need more truly free models! Happy to see #Olmo3 arrive at sizes suitable for consumer grade #GPU. The weights, data, process, logs are under #Apache2, and they run on recent llama.cpp (@ggml_org) Running #Quantization to #GGUF now… #AI #FullyOpenAI #RunLocal #Tech

Matt Turck

@mattturck

11 months ago

*Major* open source AI drop today. Can America win the Open AI race? My conversation with @natolambert and @soldni of @allen_ai about the launch of Olmo 3 00:00 – Cold Open 00:39 – Welcome & today’s big announcement 01:18 – Introducing the Olmo 3 model family 02:07 – What “base models” really are (and why they matter) 05:51 – Dolma 3: the data behind Olmo 3 08:06 – Performance vs Qwen, Gemma, DeepSeek 10:28 – What true open source means (and why it’s rare) 12:51 – Intermediate checkpoints, transparency, and why AI2 publishes everything 16:37 – Why Qwen is everywhere (including U.S. startups) 18:31 – Why Chinese labs go open source (and why U.S. labs don’t) 20:28 – Inside ATOM: the U.S. response to China’s model surge 22:13 – The rise of “thinking models” and inference-time scaling 35:58 – The full Olmo pipeline, explained simply 46:52 – Pre-training: data, scale, and avoiding catastrophic spikes 50:27 – Mid-training (tail patching) and avoiding test leakage 52:06 – Why long-context training matters 55:28 – SFT: building the foundation for reasoning 1:04:53 – Preference tuning & why DPO still works 1:10:51 – The hard part: RLVR, long reasoning chains, and infrastructure pain 1:13:59 – Why RL is so technically brutal 1:18:17 – Complexity tax vs AGI hype 1:21:58 – How everyone can contribute to the future of AI 1:27:26 – Closing thoughts

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