Most teams start their #LLM journey prototyping with #OpenAI. They quickly realize that they want to migrate to #opensource LLMs—motivated by a desire to reduce costs and own their model IP But how do you make the switch? We've got you covered https://
pbase.ai/3RxDxdF
LLMS
-
Migrating from OpenAI to Open Source LLMs: Complete Guide
By
–
-
StableLM Zephyr 3B: Powerful LLM for Edge Devices
By
–
Introducing StableLM Zephyr 3B: A new addition to StableLM, bringing powerful LLM assistants to edge devices. This 3 billion parameter LLM, with a file size 60% smaller than 7B models, allows accurate, and responsive output on a variety of devices without requiring high-end
-
Multi-modal RAG Public Benchmark Released
By
–
Multi-modal RAG public benchmark Multi-modal LLMs are one of the most important emerging trends for the coming year. They promise to unlock new types of Q+A assistants over visual content, but multi-modal RAG architectures remain uncertain. We are releasing a small
-
ChatGPT Classic 4 Features and Content Moderation Analysis
By
–
Nah, check out the pic – ChatGPT Classic is 4 without the extra stuff. I was under the impression it might've been less neutered.
-
Fireside Chat with OpenAI at Slush Available Now
By
–
The video of our fireside chat at @SlushHQ is now available! Huge thanks to @vhankes for guiding our conversation about building with @OpenAI and the future of AI. Keep an eye (or ear!) out for ChatGPT’s cameo in a mini live demo!
-
Comparative test: Google Gemini vs ChatGPT and GPT4
By
–
Will #GoogleGemini kill #ChatGPT and #GPT4? We've seen a lot of nonsense on the subject in the last 24 hours. In my latest video, I decided to go further than the commercials to do real tests and comparisons → https://youtu.be/IqZIWEBQ_5Q And it's… Surprising
-
Measuring and Mitigating Language Model Risks for Safe Deployment
By
–
As language models continue to advance rapidly, the ability to proactively measure and mitigate potential risks is increasingly important to inform decisions about safe deployment.
In the future, we hope to apply these techniques to anticipate a broader range of societal impacts. -
New Dataset Measures Discrimination Across 70 Language Model Applications
By
–
We’re releasing a new dataset for measuring discrimination across 70 different potential applications of language models, including loan applications, visa approvals, and security clearances.
We’ve used this dataset to measure discrimination in LMs and develop new mitigations. -
Language Models Risk Assessment High-Risk Automated Decision Making
By
–
While we don’t permit or endorse the use of LMs for high-risk automated decision making, it’s crucial to anticipate the potential societal impacts and risks of these models as early as possible.