Hands-on-llms Alternatives
Similar projects and alternatives to hands-on-llms
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MLSys-NYU-2022
Slides, scripts and materials for the Machine Learning in Finance Course at NYU Tandon, 2022
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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finetuned-qlora-falcon7b-medical
Finetuning of Falcon-7B LLM using QLoRA on Mental Health Conversational Dataset
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Local-LLM-Langchain
Load local LLMs effortlessly in a Jupyter notebook for testing purposes alongside Langchain or other agents. Contains Oobagooga and KoboldAI versions of the langchain notebooks with examples.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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LLM-Finetuning-Hub
Discontinued Toolkit for fine-tuning, ablating and unit-testing open-source LLMs. [Moved to: https://github.com/georgian-io/LLM-Finetuning-Toolkit]
hands-on-llms reviews and mentions
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Where to start
There are 3 courses that I usually recommend to folks looking to get into MLE/MLOps that already have a technical background. The first is a higher-level look at the MLOps processes, common challenges and solutions, and other important project considerations. It's one of Andrew Ng's courses from Deep Learning AI but you can audit it for free if you don't need the certificate: - Machine Learning in Production For a more hands-on, in-depth tutorial, I'd recommend this course from NYU (free on GitHub), including slides, scripts, full-code homework: - Machine Learning Systems And the title basically says it all, but this is also a really good one: - Hands-on Train and Deploy ML Pau Labarta, who made that last course, actually has a series of good (free) hands-on courses on GitHub. If you're interested in getting started with LLMs (since every company in the world seems to be clamoring for them right now), this course just came out from Pau and Paul Iusztin: - Hands-on LLMs For LLMs I also like this DLAI course (that includes Prompt Engineering too): - Generative AI with LLMs It can also be helpful to start learning how to use MLOps tools and platforms. I'll suggest Comet because I work there and am most familiar with it (and also because it's a great tool). Cloud and DevOps skills are also helpful. Make sure you're comfortable with git. Make sure you're learning how to actually deploy your projects. Good luck! :)
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iusztinpaul/hands-on-llms is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of hands-on-llms is Jupyter Notebook.
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