[HN Gopher] Show HN: AdalFlow: The library to build and auto-opt...
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Show HN: AdalFlow: The library to build and auto-optimize any LLM
task pipeline
Author : meame2010
Score : 11 points
Date : 2024-08-18 15:10 UTC (7 hours ago)
(HTM) web link (github.com)
(TXT) w3m dump (github.com)
| meame2010 wrote:
| LLM applications are messy, but AdalFlow has made it elegant!
|
| 0.2.0 release highlight a unified auto-differentiative framework
| where you can perform both instruction and few-shot optimization.
| Along with our own research, "Learn-to-Reason Few-shot In-context
| Learning" and "Text-Grad 2.0", AdalFlow optimizer converge
| faster, more token efficient, and with better accuracy than
| optimization-focused frameworks like Dspy and text-grad.
| jackmpcollins wrote:
| Is AdalFlow also focused on automated prompt optimization or is
| it broader in scope? It looks like there are also some features
| around evaluation. I'd be really interested to see a comparison
| between AdalFlow, DSPy [0], LangChain [1] and magentic [2]
| (package I've created, narrower in scope).
|
| [0] https://github.com/stanfordnlp/dspy
|
| [1] https://github.com/langchain-ai/langchain
|
| [2] https://github.com/jackmpcollins/magentic
| meame2010 wrote:
| We are broader. We have essential building blocks for RAG,
| Agents. But also made whatever you build possible to auto-
| optimize. You can think of us as the library to do in-context
| learning. Just like PyTorch is for model-training.
|
| Our benchmark has compared with Dspy and Text-
| grad(https://github.com/zou-group/textgrad)
|
| We have better accuracy, more token-efficient, and faster
| convergence speed. We are publishing three research papers to
| explain this better to researchers.
|
| https://adalflow.sylph.ai/use_cases/question_answering.html
|
| We will compare with these optimization libraries but wont
| compare with libraries like LangChain or LlamaIndex. As they
| simply dont have optimization and it is pain to build on
| them.
|
| Hope this make sense
| jackmpcollins wrote:
| Thanks for the explanation! Do you see auto-optimization as
| something that is useful for every use case or just some?
| And what determines when this is useful vs not?
| meame2010 wrote:
| I would say its useful for all production-grad
| application.
|
| Trainer.diagnose helps you get a final eval score across
| different splits of datasets: train, val, test, and it
| logs all errors, including format errors so that you can
| manually diagnose and to decide if the evaluation is too
| low that you need further text-grad optimization.
|
| if there is still a big gap between your optimized prompt
| vs performance on a more advanced model with the same
| prompt (say gpt4o), then you can use our "Learn-to-reason
| few-shot" to create demonstration from the advanced model
| to further close the performance gap. We have use cases
| optimized the performance all the way from 60% to 94% on
| gpt3.5 and the gpt4o has 98%.
|
| We will give users some guideline in general.
|
| We are the only library provides "diagnose" and "debug"
| feature and a clear optimization goal.
| meame2010 wrote:
| https://github.com/SylphAI-Inc/AdalFlow
| meame2010 wrote:
| AdalFlow is named in honor of Ada Lovelace, the pioneering female
| mathematician who first recognized that machines could do more
| than just calculations. As a female-led team, we aim to inspire
| more women to enter the AI field.
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