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📢 New model! 📢 As a festive treat, we’re giving pro users access to the beta version of our improved model. 🎄🎵 Generate your best music tracks yet from short and long descriptive prompts. Currently outputs are 45 seconds. Much longer soon... 🧵

Stable Audio

2,727 subscribers

39,539 просмотров • 2 лет назад •via X (Twitter)

Наука и технологии Музыка Образование

Anya Rossi• Live Now

Private livecam show

Комментарии: 10

Фото профиля Stable Audio

Stable Audio2 лет назад

We’ll roll out full access to the new model to all users in the new year.

Фото профиля Stable Audio

Stable Audio2 лет назад

You can try the new beta model by selecting ‘stable-audio-audiosparx-v1-1’ in the model selection window.

Фото профиля Stable Audio

Stable Audio2 лет назад

Try prompt: UK garage, Rhodes piano chords and melodies, walking double bass bass-line, MPC drums, syncopated percussion, beautiful, relaxing, summery, 130 BPM

Фото профиля Stable Audio

Stable Audio2 лет назад

Try prompt: Piano, beautiful, clean, soft lofi beat

Фото профиля Stable Audio

Stable Audio2 лет назад

Try prompt: Trap, a lovely and cute synth arpeggio, short 808 kick stabs, rattling 808 hi hats, fluffy synth pads, cute marimba and toy piano, R&B vocal chops, major key, beautiful, cute, well arranged, anime inspired, 130 BPM

Фото профиля Stable Audio

Stable Audio2 лет назад

Try prompt: Indie rock, strings, drum kit, electric bass, choir, synthesizer, percussion, shaker, tambourine, melancholic, beautiful, low-key, 110 BPM

Фото профиля Stable Audio

Stable Audio2 лет назад

Try prompt: Chilled house music, relaxed, inspiring

Фото профиля Darryl Mason

Darryl Mason2 лет назад

@amli_art Impressive!

Фото профиля Harmeet Gabha 🇦🇺

Harmeet Gabha 🇦🇺2 лет назад

Loop feature??

Фото профиля morgan —

morgan —2 лет назад

godly

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🎥 This video and the music is AI-generated for the Suprana brand & $SUP token. 💰 We’re offering a $500 prize to the best community made version! 🏆 Only one winner will be selected. 📢 Post your video as a reply to this comment for a chance to win. ✨ Show us your creativity and help shape the future of Please like and retweet this message till it reached to the best person who can create best video music. #SUP #SUPRANA $SUP $SUPRANA

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Tencent Hy

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New Short Course: Getting Structured LLM Output! Learn how to get structured outputs from your LLM applications in this course, built in partnership with .txt, and taught by Will Kurt, a Founding Engineer, and , Developer Relations Engineer. It's challenging for software to automatically parse through an LLM's freeform text outputs. Structured outputs—like JSON—solve this by converting natural language into consistent, clear, data that a machine can read and process. This course teaches you how to generate structured outputs while building several use cases, including a social media analysis agent. You’ll learn about structured outputs and efficient ways to generate outputs in your defined schema or format. You’ll begin by using structured output APIs, then use re-prompting libraries like “instructor” to generate structured output. Finally, you’ll learn how constrained decoding works; this is a very clever technique in which constraints are applied on each subsequent token generated, blocking any tokens that don’t fit your defined schema. In detail, you’ll: - Learn why structured outputs are important, how they allow for scalable software development, and the different approaches to generate them, including vendor-provided APIs, re-prompting libraries, and structured generation. - Build a simple social media agent using OpenAI’s structured output API, learn how to define a model's desired structured output using Pydantic, and perform basic programming with your outputs, such as importing structured data into a data frame using pandas. - Learn how to use the open-source library "instructor," which checks the structured output of the model and re-prompts the model until it validates the desired output, and explore the limitations of this approach. - Understand how structured generation by the “outlines” library works by modifying LLM logits, on a per-generated-token basis based on the desired format, to give a particular output structure. - Learn how regular expressions, which outlines works with, are represented as finite-state machines, and how they can be used to develop a range of structured outputs beyond JSON. By the end of this course, you’ll have broadened your knowledge of the approaches you can use to get structured outputs from your LLM applications. Please sign up here:

Andrew Ng

89,792 просмотров • 1 год назад