Top 14 Python Falcon Projects
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hug
Embrace the APIs of the future. Hug aims to make developing APIs as simple as possible, but no simpler.
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webargs
A friendly library for parsing HTTP request arguments, with built-in support for popular web frameworks, including Flask, Django, Bottle, Tornado, Pyramid, webapp2, Falcon, and aiohttp.
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InfluxDB
Collect and Analyze Billions of Data Points in Real Time. Manage all types of time series data in a single, purpose-built database. Run at any scale in any environment in the cloud, on-premises, or at the edge.
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hands-on-llms
🦖 𝗟𝗲𝗮𝗿𝗻 about 𝗟𝗟𝗠𝘀, 𝗟𝗟𝗠𝗢𝗽𝘀, and 𝘃𝗲𝗰𝘁𝗼𝗿 𝗗𝗕𝘀 for free by designing, training, and deploying a real-time financial advisor LLM system ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 𝘷𝘪𝘥𝘦𝘰 & 𝘳𝘦𝘢𝘥𝘪𝘯𝘨 𝘮𝘢𝘵𝘦𝘳𝘪𝘢𝘭𝘴
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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LLM-Finetuning-Hub
Repository that contains LLM fine-tuning and deployment scripts along with our research findings.
Project mention: Show HN: Finetuning LLMs: Open-source vs. Close-source | news.ycombinator.com | 2023-10-08Hello all,
I have been working on benchmarking different LLMs -- both open-source and closed-source.
Repo: https://github.com/georgian-io/LLM-Finetuning-Hub
Precisely, I am comparing their out-of-the-box capabilities (prompting) and their fine-tuned conterparts!
So far, the following models have been benchmarked:
Open-Source:
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Onboard AI
Learn any GitHub repo in 59 seconds. Onboard AI learns any GitHub repo in minutes and lets you chat with it to locate functionality, understand different parts, and generate new code. Use it for free at www.getonboard.dev.
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See Spectree for 1-4 for Flask, Flask also allows async if not see Quart and Quart-Schema. 6. It is not faster than Flask for production apps - only micro benchmarks.
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And for falconpy: https://github.com/CrowdStrike/falconpy/wiki/Identity-Protection
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falcon-apispec
apispec plugin that generates OpenAPI specification (aka Swagger Docs) for Falcon web applications.
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Falcon Toolkit is an all in one toolkit designed to make your Falcon life much easier. It is built on top of Caracara.
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
Python Falcon related posts
Index
What are some of the best open-source Falcon projects in Python? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | hug | 6,741 |
| 2 | webargs | 1,345 |
| 3 | truss | 741 |
| 4 | Secure | 668 |
| 5 | hands-on-llms | 462 |
| 6 | LLM-Finetuning-Hub | 444 |
| 7 | Finetune_LLMs | 403 |
| 8 | spectree | 294 |
| 9 | falconpy | 263 |
| 10 | deny | 83 |
| 11 | ansible_collection_falcon | 71 |
| 12 | falcon-apispec | 43 |
| 13 | caracara | 32 |
| 14 | cses2humio | 6 |