1 year of experience
👋🏻Hi there folks, i'm here to produce the backoffice and productive environment of the future where multiple agents interact to produce an office's worth of knowledge. Specifically i'm focussed on simple interfaces to deliver highly specific and precise business intelligence , creating decision support systems based on these & then AI-augmented executions of investment theses. - join me on huggingface : https://www.huggingface.co/multitransformer - join my build-in-public discord : https://discord.gg/VqTxc76K3u - contribute here : https://github.com/tonic-ai
I am a full-time professional Data Scientist and Software Developer with 3 years of experience working with MNCs and Startups. I am particularly excited about the opportunity to design and implement the infrastructure required to train and run inference of models at scale, build state-of-the-art deep learning models, to create real-time systems for model serving. Furthermore, I have experience wrangling data from disparate sources and building production-ready ML models and systems.
Who is this for? This isn't a toy; it's a tool designed for dedicated musicians who see technology as an extension of their craft. If you're not afraid to embrace AI to enhance your creative output, then Sonic Meow is made for you. What Does It Do? Welcome to the future of remixing. Sonic Meow takes your original song, slices it, dices it, and reassembles it into something entirely new. And don't worry about jarring transitions—our sophisticated algorithm ensures your remix is a seamless auditory experience. How It Works Upload Your Track: Simply load up your audio file and let Sonic Meow take the reins. Set the BPM: Make sure you know your song's tempo. Input the Beats Per Minute (BPM) to keep everything in sync. Customize Your Preferences: Set the number of iterations, prompt duration, and min-max output duration to shape your remix the way you envision it. Seamless Splicing: Our intelligent algorithm keeps track of the song's bars, making sure each remix starts and stops at just the right moments. Hit Generate: Once you've set your parameters, click 'Generate' to craft your unique remix. Unique Every Time Worried about repetitive output? Fear not! Our semi-randomization feature ensures that no two remixes are ever the same—even when using identical settings. Why Wait? Start Remixing Now Experience a new level of creative freedom with Sonic Meow. Break barriers, push boundaries, and redefine what's possible in the realm of music production.
We attempted to instill the deterministic, rule-based reasoning found in ELIZA into a more advanced, probabilistic model like an LLM. This serves a dual purpose: To introduce a controlled variable in the form of ELIZA's deterministic logic into the more "fuzzy" neural network-based systems. To create a synthetic dataset that can be used for various Natural Language Processing (NLP) tasks, beyond fine-tuning the LLM. [ https://huggingface.co/datasets/MIND-INTERFACES/ELIZA-EVOL-INSTRUCT ] [ https://www.kaggle.com/code/wjburns/pippa-filter/ ] ELIZA Implementation: We implemented the script meticulously retaining its original transformational grammar and keyword matching techniques. Synthetic Data Generation: ELIZA then generated dialogues based on a seed dataset. These dialogues simulated both sides of a conversation and were structured to include the reasoning steps ELIZA took to arrive at its responses. Fine-tuning: This synthetic dataset was then used to fine-tune the LLM. The LLM learned not just the structure of human-like responses but also the deterministic logic that went into crafting those responses. Validation: We subjected the fine-tuned LLM to a series of tests to ensure it had successfully integrated ELIZA's deterministic logic while retaining its ability to generate human-like text. Challenges Dataset Imbalance: During the process, we encountered issues related to data imbalance. Certain ELIZA responses occurred more frequently in the synthetic dataset, risking undue bias. We managed this through rigorous data preprocessing. Complexity Management: Handling two very different types of language models—rule-based and neural network-based—posed its unique set of challenges. Significance This project offers insights into how the strength of classic models like ELIZA can be combined with modern neural network-based systems to produce a model that is both logically rigorous and contextually aware.
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