One afternoon in early 2017, at Facebook’s headquarters in Menlo Park, Calif., an engineer named Tommer Leyvand sat in a conference room with a smartphone standing on the brim of his baseball cap. Rubber bands helped anchor it in place with the camera facing out. The absurd hat-phone, a particularly uncool version of the future, contained a secret tool known only to a small group of employees. What it could do was remarkable.
… Mr. Leyvand turned toward a man across the table from him. The smartphone’s camera lens — round, black, unblinking — hovered above Mr. Leyvand’s forehead like a Cyclops eye as it took in the face before it. Two seconds later, a robotic female voice declared, “Zach Howard.”
“That’s me,” confirmed Mr. Howard, a mechanical engineer. — Read More
Daily Archives: September 18, 2023
Why Open Source AI Will Win
There’s a popular floating theory on the Internet that a combination of the existing foundation model companies will be the end game for AI.
In the near future, every company will rent a “brain” from a model provider, such as OpenAI/Anthropic, and build applications that build on top of its cognitive capabilities.
In other words, AI is shaping up to be an oligopoly of sorts, with only a small set of serious large language model (LLM) providers.
I don’t think this could be farther from the truth. I truly believe that open source will have more of an impact on the future of LLMs and image models than the broad public believes. — Read More
BlockChain and Web3
15 times Faster than Llama 2: Introducing DeciLM – NAS-Generated LLM with Variable GQA
As the deep learning community continues to push the boundaries of Large Language Models (LLMs), the computational demands of these models have surged exponentially for both training and inference. This escalation has not only led to increased costs and energy consumption but also introduced barriers to their deployment and scalability. Achieving a balance between model performance, computational efficiency, and latency has thus become a focal point in recent LLM development.
Within this landscape, we are thrilled to introduce DeciLM 6B, a permissively licensed foundation LLM, and DeciLM 6B-Instruct, fine-tuned from DeciLM 6B for instruction-following use cases. With 5.7 billion parameters, DeciLM 6B delivers a throughput that’s 15 times higher than Llama 2 7B while maintaining comparable quality. Impressively, despite having significantly fewer parameters, DeciLM 6B and DeciLM 6B-Instruct consistently rank among the top-performing LLMs in the 7 billion parameter category across various LLM evaluation tasks. Our models thus establish a new benchmark for inference efficiency and speed. The hallmark of DeciLM 6B lies in its unique architecture, generated using AutoNAC, Deci’s cutting-edge Neural Architecture Search engine, to push the efficient frontier. Moreover, coupling DeciLM 6B with Deci’s inference SDK results in a substantial throughput enhancement. — Read More
Rick's Cafe AI 