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Introducing OpenChatKit 🚀 - The first open-source alternative to ChatGPT! A team of ex-OpenAI fellows at Together have released a 20B chat-GPT model, fine-tuned for chat using EleutherAI's GPT-NeoX-20B, with over 43 million instructions under the Apache-2.0 license. >>>

Itamar Golan 🤓

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555,498 görüntüleme • 3 yıl önce •via X (Twitter)

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This instruction-tuned large language model has been optimized for chat on 100% carbon-negative compute. OpenChatKit includes four essential components: >>>

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- An instruction-tuned large language model, fine-tuned for chat using EleutherAI's GPT-NeoX-20B with over 43 million instructions on carbon-negative compute. - Customization recipes that help fine-tune the model to deliver high accuracy results for specific tasks.

Itamar Golan 🤓 profil fotoğrafı

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- An extensible retrieval system that allows you to add information from a document repository, API or other live-updating information sources at inference time. - A moderation model, fine-tuned from GPT-JT-6B, that filters which questions the bot responds to. Holy cow!!! 🐄

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Github - Demo -

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Follow on Patreon for Unique & Daily AI content-

we are here because we are here because we are - profil fotoğrafı

we are here because we are here because we are -3 yıl önce

How is it different from ChatGPT? Please explain to me like I'm 5.

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You have access to the model's weights and training data.

Djamé.. profil fotoğrafı

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things are going way too fast these days...

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Enjoy the ride 😎

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Congrats to the team at Together for releasing OpenChatKit, Keep up the great work!

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A voice agent powered by gpt-oss. Running locally on my macBook. Demo recorded in a Waymo with WiFi turned off. I'm still on my space game voice AI kick, obviously. Code link below. For conversational voice AI, you want to set the gpt-oss reasoning behavior to "low". (The default is "medium".) Notes on how to do that and a jinja template you can use are in the repo. The LLM in the demo video is the big, 120B version of gpt-oss. You can use the smaller, 20B model for this, of course. But OpenAI really did a cool thing here designing the 120B model to run in "just" 80GB of VRAM. And the llama.cpp mlx inference is fast: ~250ms TTFT. Running a big model on-device feels like a time warp into the future of AI.

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