our next #ai-in-action club is starting now: on @lateinteraction
’s DSPy! led by @ProgramWithAi and @kbal11
CODE
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AI-in-Action Club Launches DSPy Framework Training Session
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GGUF Quantizations Ready for Hermes-3-Llama Model Testing
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GGUF quants are also ready if you want to test the model. @bartowski1182 already made fancy imatrix ones. Love the new model tree on @huggingface
! (cc @victormustar @julien_c
) GGUF: https://
huggingface.co/mlabonne/Herme
s-3-Llama-3.1-8B-lorablated-GGUF
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Open-Source AI Models and Abliteration Techniques Explained
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Of course, special thanks to @NousResearch and @teknium for these high-quality models. Thanks to grimjim and @failspy for the abliteration techniques that were used here. You can learn more about it in my article:
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Hermes 3 LoRA-Ablated Uncensored Model Release
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A fully uncensored Hermes 3 with lorablation! The lorablated model directly answers questions without any tweaking. Here's how it was made: 1/ Create a LoRA adapter based on Llama 3.1 8B Instruct and my abliterated version. 2/ Apply it to Hermes 3 from @NousResearch using
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Building AI Workflows with Lutra for Key Insights
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What kind of key insights are you looking for? You should be able to use @Lutra_AI to build such a workflow pretty easily
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MergeKit and PruneMe: Essential Tools for Model Optimization
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No, I would recommend libraries like MergeKit or PruneMe (
https://
github.com/arcee-ai/Prune
Me
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Live Fine-Tuning Models Session with DataCamp and Google Colab
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If you want to start fine-tuning models, I'll do a live session on August 27 with @DataCamp using @GoogleColab
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Survey on Prompt Engineering Strategies and Techniques
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If you’re looking to learn more about prompt engineering, here’s a good survey paper that outlines the different types of prompt engineering (link provided in alt) Here are some of the strategies discussed:
•Chain of Thought (CoT)
•Chain of Cause (CoC)
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Tenstorrent’s Buda Hackathon Showcases AI Applications
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Photos from Tenstorrent's 'Intro to Buda' Hackathon, organized by Sebastian Phemister. Teams created an internal help chat bot, text-to-speech systems, speech-to-text applications, visual question answering systems, and image classification models. Find out more about TT-Buda
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Modular Speech-to-Speech Pipeline Achieves Sub-500ms Latency
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this modular speech-to-speech pipeline repo with <500 ms second latency (on a local GPU) is going crazy
— Thomas Wolf (@Thom_Wolf) 16 août 2024
Impressive what a well tuned pipeline of stt-llm-tts can do
cool work by @eustachelb with @reach_vb and @andi_marafioti
find the code here: https://t.co/HrbqSWQ3sj pic.twitter.com/HDkEnVntKNthis modular speech-to-speech pipeline repo with <500 ms second latency (on a local GPU) is going crazy Impressive what a well tuned pipeline of stt-llm-tts can do cool work by @eustachelb with @reach_vb and @andi_marafioti find the code here: https://
github.com/eustlb/speech-
to-speech
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