mamba
ragas
mamba | ragas | |
---|---|---|
15 | 10 | |
9,506 | 4,668 | |
15.3% | 14.1% | |
8.1 | 9.6 | |
9 days ago | 5 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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mamba
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Based: Simple linear attention language models
> how the recall can grow unbounded with no tradeoff
this? https://github.com/state-spaces/mamba/issues/175
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Mamba: The Easy Way
If you want to learn this stuff as a computer engineer, you can read the code here [0]. I find the math quite helpful.
[0]: https://github.com/state-spaces/mamba
- FLaNK Stack 05 Feb 2024
- Introduction to State Space Models (SSM)
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Fortran inference code for the Mamba state space language model
This model was discussed recently: https://news.ycombinator.com/item?id=38522428 It's a new kind of ML model architecture that can be used instead of a transformer in LLMs.
See also the original repo from the paper: https://github.com/state-spaces/mamba
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Mamba outperforms transformers "everywhere we tried"
[2] - https://github.com/state-spaces/mamba
Out of curiosity, does anyone feel as though there's any benefit to linking to reddit when we can link to whatever the link is? I for one do not click the link and read discussion on reddit - if I wanted that sort of discussion, I would browse there, not HN.
- GitHub โ State-Spaces/Mamba
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Generate valid JSON with Mamba models
The library is compatible with any auto-regressive model, not transformers. To prove our point we integrated Mamba, a new state-space model architecture, to the library. Try it out!
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[D] Thoughts on Mamba?
I ran the NanoGPT of Karparthy replacing Self-Attention with Mamba on his TinyShakespeare Dataset and within 5 minutes it started spitting out the following:
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Mamba-Chat: A Chat LLM based on State Space Models
You might have come across the paper Mamba paper in the last days, which was the first attempt at scaling up state space models to 2.8B parameters to work on language data.
ragas
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Show HN: Ragas โ the de facto open-source standard for evaluating RAG pipelines
congrats on launching! i think my continuing struggle with looking at Ragas as a company rather than an oss library is that the core of it is like 8 metrics (https://github.com/explodinggradients/ragas/tree/main/src/ra...) that are each 1-200 LOC. i can inline that easily in my app and retain full control, or model that in langchain or haystack or whatever.
why is Ragas a library and a company, rather than an overall "standard" or philosophy (eg like Heroku's 12 Factor Apps) that could maybe be more robust?
(just giving an opp to pitch some underappreciated benefits of using this library)
- FLaNK 04 March 2024
- FLaNK Stack 05 Feb 2024
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SuperDuperDB - how to use it to talk to your documents locally using llama 7B or Mistral 7B?
Also, at some point you'll need to get serious about evaluation (trust me, you will). You may be interested in https://github.com/explodinggradients/ragas
- Ragas โ Framework for RAG Evaluation
- Ragas: Open-source Evaluation framework for RAG pipelines
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Building a customer support chatbot using GPT-3.5 and lLamaIndex๐
The problem becomes worse if you want to inspect outputs from not just one, but several different queries. Luckily, there are several free open source packages such as ragas and DeepEval that can help evaluate your chatbot so you don't have to manually do it ๐
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Patterns for Building LLM-Based Systems and Products
We have build RAGAS framework for this https://github.com/explodinggradients/ragas
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[R] All about evaluating Large language models
Hi u/thecuteturtle, I am building open-source projects for evaluating LLM-based applications. Check it out https://github.com/explodinggradients/ragas and if you like to collaborate let me know :)
What are some alternatives?
miniforge - A conda-forge distribution.
deepeval - The LLM Evaluation Framework
pip - The Python package installer
chameleon-llm - Codes for "Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models".
llm.f90 - LLM inference in Fortran
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.
conda - A system-level, binary package and environment manager running on all major operating systems and platforms.
FastLoRAChat - Instruct-tune LLaMA on consumer hardware with shareGPT data
mamba-chat - Mamba-Chat: A chat LLM based on the state-space model architecture ๐
agenta - The all-in-one LLM developer platform: prompt management, evaluation, human feedback, and deployment all in one place.
spack - A flexible package manager that supports multiple versions, configurations, platforms, and compilers.
text-generation-webui-colab - A colab gradio web UI for running Large Language Models