jxnlco

8 ms·

61.
by jxnlco 3y ago
I'll fix this now!
62.
by jxnlco 3y ago
right which is why finetuning on the last one is a great save but preserves quality
63.
At the Intersection of LLMs and Kernels – Research Roundup(charlesfrye.github.io)
6 points by jxnlco 3y ago|0 comments
64.
by jxnlco 3y ago
for sure! the one thing i was surprised by was how little data gpt3.5 needed, could love for a company to try how the scaling laws work for those smaller models.
65.
by jxnlco 3y ago
I'd say its zod, but it does not have validation in the same way instructor does. Typechat has some options tho! https://github.com/microsoft/typechat/
66.
by jxnlco 3y ago
The goal of instructor is that i dont write any prompts for you, i just make the openai.ChatCompletion.create call easier to manage and let you return a pass in a pydantic object and return an instance of it, Whole codebase is 300 lines of
67.
by jxnlco 3y ago
creator of instructor here: My only goal is to make structured output easier, I want to be thought of as more like 'requests' it just makes http/io a bit easier. Rather than say, a whole app framework. Instructor only support
68.
Good LLM Validation Is Just Good Validation(jxnl.github.io)
2 points by jxnlco 3y ago|0 comments
69.
Distilling Python Functions into an LLM Finetune(jxnl.github.io)
3 points by jxnlco 3y ago|0 comments
70.
by jxnlco 3y ago
> asymmetric embeddings, instruct-based embeddings, and retrieval-rerank how would you handle a relative time range? what if you're provided a search client that does not support embeddings (maybe say, google calendar api)
71.
by jxnlco 3y ago
why not just asyncio.gather?
72.
by jxnlco 3y ago
I'd just see that as modeling {query: str, keywords: List[str]}
73.
RAG is more than just embedding search(jxnl.github.io)
151 points by jxnlco 3y ago|58 comments
74.
Improving OpenAI Function Calling Usability in Python(medium.com)
8 points by jxnlco 3y ago|0 comments
75.
Show HN: Automatic YouTube Chapters and Summaries(youtubechapters.app)
1 points by jxnlco 3y ago|0 comments
76.
Modeling Extractions with OpenAI Function Calls with Pydantic(openai-function-call.onrender.com)
2 points by jxnlco 3y ago|0 comments
77.
by jxnlco 3y ago
I mostly wrote this to be as minimally invasive as possible and no require installation (just copy paste the code) The blog and code is here: https://github.com/jxnl/openai_function_call based on a thread of notes i to
78.
Validating OpenAI's new function call with Pydantic in 50 lines of code(medium.com)
7 points by jxnlco 3y ago|1 comments
79.
by jxnlco 4y ago
mind sharing the link? perhaps something went wrong with transcriptions
80.
Show HN: Turn YouTube videos into study notes w/ Streaming, GPT, and Whisper(magic.jxnl.co)
20 points by jxnlco 4y ago|5 comments
81.
by jxnlco 4y ago
Hey HN! I just launched https://magic.jxnl.co/youtube , a small app that streamlines YouTube videos by providing powerful tools to turn video content to structured written content. Key features: Transcription: The app utiliz
82.
by jxnlco 4y ago
looks like it probs passes the source and target schema throught an LLM that generates a sql create statement. similar to https://magic.jxnl.co/data and make a request like 'write me sql to map the existing tables to a
83.
by jxnlco 4y ago
Bring your own schema and generate sql from your natural language questions.