With the open-weight release of CogVideoX-5B from THUDM, i.e. GLM team, the Video Generation Model (how about calling it VGM) field has officially became the next booming "LLM"
What does the landscape look like? What are other video generation models? This collection below is all your need.
๐AI math olympiad winner NuminaMath is here! ๐คAnnouncing New Hugging Face and Keras NLP integration โจUI overhaul to HF tokens! ๐ง Embed our dataset viewer on any webpage!
๐ Phi 3 ๐ฆ Falcon VLM ๐ค sentence-transformers v3.0 is here! Train and finetune embedding models with multi-GPU training, bf16 support, loss logging, callbacks and more! ๐ฅณ Gradio launch event 6/6! We're launching 1.0 versions of two new libraries, Python + JS client libraries to programmatically query Gradio apps, and several new features making it easier to use Gradio apps in production! โจ Tools now available in HuggingChat! Use any AI apps built by the community! ๐ฅ ๐ง ML for 3D Course Unit 3 is here! Covering Gaussian splatting, how it fits in the generative 3D pipeline, and hands-on code to build your own demo!
@01AI_Yi recently switched from a permissive & commercially friendly license, to Apache 2.0. And the community loved it! ๐
@JustinLin610 also had a poll on model license and the majority votes for Apache 2.0.
Why it is a Big Deal? โฌ๏ธ
๐ Legal Simplicity: Custom licenses need costly & time-consuming legal review. Apache 2.0 is well-known & easier for legal teams to handle.
๐ฉโ๐ป Developer-Friendly: Legal docs are a pain for devs! Apache 2.0 is well-known and tech-friendly, making it easier for non-native developers to understand the implications too.
๐ Easier Integration: Apache 2.0 is compatible with many other licenses, simplifying tasks like model merging with models of different licensing requirements.
๐ซ No Permission Needed: Custom licenses often require explicit permission and additional documentation work of filling forms, creating barriers. Apache 2.0 removes this hurdle, letting devs focus on innovation.
DeepSeekV2 is a big deal. Not only because its significant improvements to both key components of Transformer: the Attention layer and FFN layer.
It has also completed disrupted the Chines LLM market and forcing the competitors to drop the price to 1% of the original price.
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There are two key components in Transformer architecture: the self-attention layer, which captures relationships between tokens in context, and the Feed-Forward Network (FFN) layer, which stores knowledge.
DeepSeek V2 introduces optimizations to both:
Attention layer normally uses KV Cache to reduce repetitive compute, but it consumes significant GPU RAM, limiting concurrent requests. DeepSeek V2 introduces Multi-head Latent Attention (MLA), which stores only a small latent representation, resulting in substantial RAM savings.
DeepSeek V2 utilizes 162 experts instead of the usual 8 as in Mixtral. This approach segments experts into finer granularity for higher specialization and more accurate knowledge acquisition. Activating only a small subset of experts for each token, leads to efficient processing.
It disrupted the market by dropping API prices to $0.14 per 1M tokens. This dramatic reduction forced competitors like GLM, Ernie, and QWen to follow suit, lowering their prices to 1% of their original offerings. Now, users can access these APIs at 1/35th the cost of ChatGPT-4o.