grape
gpt4all
grape | gpt4all | |
---|---|---|
3 | 139 | |
482 | 64,686 | |
2.9% | 2.7% | |
6.4 | 9.8 | |
2 months ago | 4 days ago | |
Jupyter Notebook | C++ | |
MIT License | MIT License |
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grape
- Grape (Graph Representation LeArning, Predictions and Evaluation)
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Zoomable, animated scatterplots in the browser that scales over a billion points
Ideally, you'd embed the graph into 2 or 3d first, then visualize it as a scatterplot.
Visualizing the edges at scale doesnt yield nice results in general.
The way to do it is to reduce the graph to some 300d or 500d embeddings, then use TSNE/UMAP/PACMAP to reduce that to 3d. Then visualize.
My prefered way is to use some first order embedding method like GGVec in this library [1] (disclaimer I wrote it). Node2Vec and ProNE don't yield great embeddings for visualization (the first is too filamented, the second too close to the unit ball).
Another great library to do this work is GRAPE [2]. Try first-order embedding methods, or short walks on second order methods to avoid the embeddings being too filamented by long random walk sampling.
[1] https://github.com/VHRanger/nodevectors
[2] https://github.com/AnacletoLAB/grape/
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[P] We are building a curated list of open source tooling for data-centric AI workflows, looking for contributions.
For graph embeddings, there's quite a few. I'd recommend this one, but there's also this one (disclaimer: I'm the author) or this one, more of a DGL library.
gpt4all
- Show HN: I made an app to use local AI as daily driver
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Ollama Python and JavaScript Libraries
I donāt know if Ollama can do this but https://gpt4all.io/ can.
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Ask HN: How do I train a custom LLM/ChatGPT on my own documents in Dec 2023?
Gpt4all is a local desktop app with a Python API that can be trained on your documents: https://gpt4all.io/
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WyGPT: Minimal mature GPT model in C++
The readme page is cryptic. What does 'mature' mean in this context? What is the sample text a continuation of?
Hving a gif the thing in use would be great, similar to the gpt4all readme page. (https://github.com/nomic-ai/gpt4all)
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LibreChat
Check https://github.com/nomic-ai/gpt4all instead.
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OpenAI Negotiations to Reinstate Altman Hit Snag over Board Role
"I ran performance tests on two systems, here's the results of system 1, and heres the results of system 2. Summarize the results, and build a markdown table containing x,y,z rows."
"extract the reusable functions out of this bash script"
"write me a cfssl command to generate a intermediate CA"
"What is the regex for _____"
"Here are my accomplishments over the last 6 months, summarize them into a 1 page performance report."
etc etc etc
If you're not using GPT4 or some LLM as part of your daily flow you're working too hard.
Get GPT4All (https://gpt4all.io), log into OpenAI, drop $20 on your account, get a API key, and start using GPT4.
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Darbe uzdraude naudotis CHATGPT: ar cia normalu?
offline versija, nors ir ne tokia pažengus - https://github.com/nomic-ai/gpt4all ; https://gpt4all.io/index.html
- GPT4All: An ecosystem of open-source on-edge large language models - by Nomic AI
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Why use OpenAI's ChatGPT3.5 online service, if you can instead host your own local llama?
Take a look at https://gpt4all.io, their docs are pretty awesome
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Ask HN: Are you using a local LLM? If yes, what for?
I run one. I built an iMessage-like frontend to it using plain JS and a Python websocket backend. I mostly just use it for curiosity and playing with different prompts. I only have 16GB of RAM to dedicate to it, so I use an 8B parameter model which is enough for fun and chitchat, but I don't find it good enough to replace ChatGPT.
https://github.com/nomic-ai/gpt4all
What are some alternatives?
deodel - A mixed attributes predictive algorithm implemented in Python.
llama.cpp - LLM inference in C/C++
refinery - The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact.
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
deepscatter - Zoomable, animated scatterplots in the browser that scales over a billion points
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks
dgl - Python package built to ease deep learning on graph, on top of existing DL frameworks.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
nanocube
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM
cleanlab - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
TavernAI - Atmospheric adventure chat for AI language models (KoboldAI, NovelAI, Pygmalion, OpenAI chatgpt, gpt-4)