khoj
bench-warmers
khoj | bench-warmers | |
---|---|---|
50 | 6 | |
4,858 | 54 | |
2.8% | - | |
9.9 | 9.7 | |
about 7 hours ago | 19 days ago | |
Python | Python | |
GNU Affero General Public License v3.0 | MIT License |
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.
khoj
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Show HN: I made an app to use local AI as daily driver
There are already several RAG chat open source solutions available. Two that immediately come to mind are:
Danswer
https://github.com/danswer-ai/danswer
Khoj
https://github.com/khoj-ai/khoj
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Ask HN: How do I train a custom LLM/ChatGPT on my own documents in Dec 2023?
I'm a fan of Khoj. Been using it for months. https://github.com/khoj-ai/khoj
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You probably don’t need to fine-tune LLMs
https://github.com/khoj-ai/khoj
This is the easiest I found, on here too.
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Show HN: Khoj – Chat Offline with Your Second Brain Using Llama 2
Thanks for the feedback. Does your machine have a GPU? 32GB CPU RAM should be enough but GPU speeds up response time.
We have fixes for the seg fault[1] and improvement to the query speed[2] that should be released by end of day today[3].
Update khoj to version 0.10.1 with pip install --upgrade khoj-assistant to see if that improves your experience.
The number of documents/pages/entries doesn't scale memory utilization as quickly and doesn't affect the search, chat response time as much
[1]: The seg fault would occur when folks sent multiple chat queries at the same time. A lock and some UX improvements fixed that
[2]: The query time improvements are done by increasing batch size, to trade-off increased memory utilization for more speed
[3]: The relevant pull request for reference: https://github.com/khoj-ai/khoj/pull/393
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A Review: Using Llama 2 to Chat with Notes on Consumer Hardware
We recently integrated Llama 2 into Khoj. I wanted to share a short real-world evaluation of using Llama 2 for the chat with docs use-cases and hear which models have worked best for you all. The standard benchmarks (ARC, HellaSwag, MMLU etc.) are not tuned for evaluating this
- FLaNK Stack Weekly for 17 July 2023
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An open source AI search + chat assistant for your Notion workspace
Self-host your Notion assistant using the instructions here. You'll need Python >= 3.8 to get started.
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When will we get JARVIS?
Here's an early example: https://github.com/khoj-ai/khoj
bench-warmers
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What to do next?
i have more ideas than I know what to do with, help yourself: https://github.com/dmarx/bench-warmers
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Any ideas for NLP end-to-end projects or blogs for a beginner with a linguistics background to boost their CV?
you're welcome to help yourself to my ideas (no guarantees that they're any good or even comprehensible, I do a lot of my brainstorming while high). here's my brainstorming space, scroll down for a categorized ToC: https://github.com/dmarx/bench-warmers
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[R] LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention
I've decided to just lean into it and am literally just giving my ideas away. https://github.com/dmarx/bench-warmers
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Using Github to write my notes has helped me retain knowledge immensely.
it might sound like a lot, but it's actually really lightweight and easy to use. Check it out: https://github.com/dmarx/bench-warmers
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We are the developers behind pandas, currently preparing for the 2.0 release :) AMA
you've sort of become victims of your own success: as another pandas dev mentioned, you want to preserve backwards compatibility and this significantly complicates any restructuring. I'm sympathetic and am not sure what the best solution here would be. I had this idea last night but i'm not sure I like this approach either.
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Need help on finding an area where machine learning is applicable on day-to-day life but not implemented already
To be clear, i'm talking about e.g. vision impaired, hearing impaired, etc. Here's an example of a project idea in this space (possibly a bit more ambitious than what you're looking for but if you think you could tackle this I encourage you to take a stab at it): https://github.com/dmarx/bench-warmers/blob/main/automated-video-description.md
What are some alternatives?
obsidian-smart-connections - Chat with your notes & see links to related content with AI embeddings. Use local models or 100+ via APIs like Claude, Gemini, ChatGPT & Llama 3
notes
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
LLaMA-Adapter - [ICLR 2024] Fine-tuning LLaMA to follow Instructions within 1 Hour and 1.2M Parameters
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
python-bigquery-pandas - Google BigQuery connector for pandas
llama-cpp-python - Python bindings for llama.cpp
pandas-stubs - Public type stubs for pandas
obsidian-ava - Quickly format your notes with ChatGPT in Obsidian
obsidian-omnisearch - A search engine that "just works" for Obsidian. Supports OCR and PDF indexing.
logseq-plugin-gpt3-openai - A plugin for GPT-3 AI assisted note taking in Logseq
scikit-learn - scikit-learn: machine learning in Python