dspy
skypilot
dspy | skypilot | |
---|---|---|
22 | 34 | |
10,820 | 5,675 | |
17.5% | 3.6% | |
9.9 | 9.8 | |
6 days ago | 6 days ago | |
Python | Python | |
MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
dspy
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Computer Vision Meetup: Develop a Legal Search Application from Scratch using Milvus and DSPy!
Legal practitioners often need to find specific cases and clauses across thousands of dense documents. While traditional keyword-based search techniques are useful, they fail to fully capture semantic content of queries and case files. Vector search engines and large language models provide an intriguing alternative. In this talk, I will show you how to build a legal search application using the DSPy framework and the Milvus vector search engine.
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Pydantic Logfire
I’ve observed that Pydantic - which we’ve used for years in our API stack - has become very popular in LLM applications, for its type-adjacent features. It serves as a foundational technology for prompting libraries like [DSPy](https://github.com/stanfordnlp/dspy) which are abstracting “up the stack” of LLM apps. (some opinions there)
Operating AI apps reveals a big challenge, in that debugging probabilistic code paths requires more than the usual introspective abilities, and in an environment where function calls can have very real monetary impact we have to be able to see what’s happening in the runtime. See LangChain’s hosted solution (can’t recall the name) that allows an operator to see prompts and responses “on the wire”. (It just occurred to me that Langchain and Pydantic have a lot in common here, in approach.)
Having a coupling between Pydantic - which is *just about* the data layer itself - and an observability tool seems very interesting to me, and having this come from the folks who built it does not seem unreasonable. WRT open source and monetization, I would be lying if I said I wasn’t a little worried - given the recent few months - but I am choosing to see this in a positive light, given this team’s “believability weight” (to overuse Dalio) and history of delivering solid and really useful tooling.
- Ask HN: Most efficient way to fine-tune an LLM in 2024?
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Princeton group open sources "SWE-agent", with 12.3% fix rate for GitHub issues
DSPy is the best tool for optimizing prompts [0]: https://github.com/stanfordnlp/dspy
Think of it as a meta-prompt optimizer, it uses a LLM to optimize your prompts, to optimize your LLM.
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Winner of the SF Mistral AI Hackathon: Automated Test Driven Prompting
Isn’t this just a very naive implementation of what DsPY does?
https://github.com/stanfordnlp/dspy
I don’t understand what is exceptional here.
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Show HN: Fructose, LLM calls as strongly typed functions
Have you done any comparison with DSPy ? (https://github.com/stanfordnlp/dspy)
Feels very similiar to DSPy except you dont have optimizations yet. But I like your API and the programming model your are enforcing through this.
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AI Prompt Engineering Is Dead
I'm interested in hearing if anyone has used DSPy (https://github.com/stanfordnlp/dspy) just for prompt optimization for GPT-3.5 or GPT-4. Was it worth the effort and much better than manual prompt iteration? Was the optimized prompt some weird incantation? Any other insights?
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Ask HN: Are you using a GPT to prompt-engineer another GPT?
You should check out x.com/lateinteraction's DSPy — which is like an optimizer for prompts — https://github.com/stanfordnlp/dspy
- SuperDuperDB - how to use it to talk to your documents locally using llama 7B or Mistral 7B?
- FLaNK Stack Weekly for 12 September 2023
skypilot
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Alternative clouds are booming as companies seek cheaper access to GPUs
Skypilot is worth a mention here:
https://github.com/skypilot-org/skypilot
Open source CLI to deploy multiple gpu vm’s on all major cloud providers, with an option to use spot pricing with 1 cheap vm used as a controller to always make sure you have the most inexpensive deployment available with failover and load balancing.
It’s like beating the cloud providers at their own game I wouldn’t be surprised if they banned it.
- Ask HN: Most efficient way to fine-tune an LLM in 2024?
- SGLang: Fast and Expressive LLM Inference with RadixAttention for 5x Throughput
- Serving Your Private Code Llama-70B with API, Chat, and VSCode Access
- A new old kind of R&D lab
- New Recipe: Serving Llama-2 with VLLM's OpenAI-Compatible API Server
- Train Your Own Vicuna on Llama-2
- Run Llama2 in your cloud privately
- SkyPilot: Run LLMs, AI, and Batch jobs on any cloud
- Chat with your documents using LocalGPT and SkyPilot
What are some alternatives?
semantic-kernel - Integrate cutting-edge LLM technology quickly and easily into your apps
reflex - 🕸️ Web apps in pure Python 🐍
open-interpreter - A natural language interface for computers
tiktoken - tiktoken is a fast BPE tokeniser for use with OpenAI's models.
playground - Play with neural networks!
skyplane - 🔥 Blazing fast bulk data transfers between any cloud 🔥
MLflow - Open source platform for the machine learning lifecycle
bricks - Open-source natural language enrichments at your fingertips.
FastMJPG - FastMJPG is a command line tool for capturing, sending, receiving, rendering, piping, and recording MJPG video with extremely low latency. It is optimized for running on constrained hardware and battery powered devices.
min - A fast, minimal browser that protects your privacy
prompt-engine-py - A utility library for creating and maintaining prompts for Large Language Models
pynimate - Python package for statistical data animations