Top Tweets for #Olmo3
إطلاق نموذج Olmo 3 الجديد، 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

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...😅🤯
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. 🧵

🎉 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
*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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