RWKV-LM
gpt4all


RWKV-LM | gpt4all | |
---|---|---|
85 | 146 | |
13,137 | 72,520 | |
2.2% | 1.6% | |
9.1 | 9.8 | |
12 days ago | 1 day ago | |
Python | C++ | |
Apache License 2.0 | 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.
RWKV-LM
-
Ask HN: Is anybody building an alternative transformer?
You can see all the development directly from them: https://github.com/BlinkDL/RWKV-LM
Last week version 7 was released and every time they make significant improvements.
-
Do LLMs need a context window?
https://github.com/BlinkDL/RWKV-LM#rwkv-discord-httpsdiscord... lists a number of implementations of various versions of RWKV.
https://github.com/BlinkDL/RWKV-LM#rwkv-parallelizable-rnn-w... :
> RWKV: Parallelizable RNN with Transformer-level LLM Performance (pronounced as "RwaKuv", from 4 major params: R W K V)
> RWKV is an RNN with Transformer-level LLM performance, which can also be directly trained like a GPT transformer (parallelizable). And it's 100% attention-free. You only need the hidden state at position t to compute the state at position t+1. You can use the "GPT" mode to quickly compute the hidden state for the "RNN" mode.
> So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding (using the final hidden state).
> "Our latest version is RWKV-6,*
- People who've used RWKV, whats your wishlist for it?
- Paving the way to efficient architectures: StripedHyena-7B
-
Understanding Deep Learning
That is not true. There are RNNs with transformer/LLM-like performance. See https://github.com/BlinkDL/RWKV-LM.
-
Q-Transformer: Scalable Reinforcement Learning via Autoregressive Q-Functions
This is what RWKV (https://github.com/BlinkDL/RWKV-LM) was made for, and what it will be good at.
Wow. Pretty darn cool! <3 :'))))
-
Personal GPT: A tiny AI Chatbot that runs fully offline on your iPhone
Thanks for the support! Two weeks ago, I'd have said longer contexts on small on-device LLMs are at least a year away, but developments from last week seem to indicate that it's well within reach. Once the low hanging product features are done, I think it's a worthy problem to spend a couple of weeks or perhaps even months on. Speaking of context lengths, recurrent models like RWKV technically have infinite context lengths, but in practice the context slowly fades away after a few thousands of tokens.
-
"If you see a startup claiming to possess top-secret results leading to human level AI, they're lying or delusional. Don't believe them!" - Yann LeCun, on the conspiracy theories of "X company has reached AGI in secret"
This is the reason there are only a few AI labs, and they show little of the theoretical and scientific understanding you believe is required. Go check their code, there's nothing there. Even the transformer with it's heads and other architectural elements turns out to not do anything and it is less efficient than RNNs. (see https://github.com/BlinkDL/RWKV-LM)
-
The Secret Sauce behind 100K context window in LLMs: all tricks in one place
I've been pondering the same thing, as simply extending the context window in a straightforward manner would lead to a significant increase in computational resources. I've had the opportunity to experiment with Anthropics' 100k model, and it's evident that they're employing some clever techniques to make it work, albeit with some imperfections. One interesting observation is that their prompt guide recommends placing instructions after the reference text when inputting lengthy text bodies. I noticed that the model often disregarded the instructions if placed beforehand. It's clear that the model doesn't allocate the same level of "attention" to all parts of the input across the entire context window.
Moreover, the inability to cache transformers makes the use of large context windows quite costly, as all previous messages must be sent with each call. In this context, the RWKV-LM project on GitHub (https://github.com/BlinkDL/RWKV-LM) might offer a solution. They claim to achieve performance comparable to transformers using an RNN, which could potentially handle a 100-page document and cache it, thereby eliminating the need to process the entire document with each subsequent query. However, I suspect RWKV might fall short in handling complex tasks that require maintaining multiple variables in memory, such as mathematical computations, but it should suffice for many scenarios.
On a related note, I believe Anthropics' Claude is somewhat underappreciated. In some instances, it outperforms GPT4, and I'd rank it somewhere between GPT4 and Bard overall.
-
Meta's plan to offer free commercial AI models puts pressure on Google, OpenAI
> The only reason open-source LLMs have a heartbeat is they’re standing on Meta’s weights.
Not necessarily.
RWKV, for example, is a different architecture that wasn't based on Facebook's weights whatsoever. I don't know where BlinkDL (the author) got the training data, but they seem to have done everything mostly independently otherwise.
https://github.com/BlinkDL/RWKV-LM
disclaimer: I've been doing a lot of work lately on an implementation of CPU inference for this model, so I'm obviously somewhat biased since this is the model I have the most experience in.
gpt4all
-
6 Easy Ways to Run LLM Locally + Alpha
https://github.com/nomic-ai/gpt4all support OS: Windows, Linux, MacOS
-
Top 8 OpenSource Tools for AI Startups
Generative AI is hot, and ChatGPT4all is an exciting open-source option. It allows you to run your own language model without needing proprietary APIs, enabling a private and customizable experience.
-
Forget ChatGPT: why researchers now run small AIs on their laptops
GPT4All for an even easier gui
https://github.com/nomic-ai/gpt4all
-
The 6 Best LLM Tools To Run Models Locally
GPT4ALL is built upon privacy, security, and no internet-required principles. Users can install it on Mac, Windows, and Ubuntu. Compared to Jan or LM Studio, GPT4ALL has more monthly downloads, GitHub Stars, and active users.
-
Llama 3.1 web search integrated into GPT4All Beta
From the moment Llama 3.1 was released, GPT4All developers have been working hard to make a beta version of tool calling available. We're happy to announce that the beta is now ready. The first tool is web search implemented through brave.com just as in the Llama 3.1 paper.
A wiki has been made to walk users through the setup here: https://github.com/nomic-ai/gpt4all/wiki/Web-Search-Beta-Rel...
Join us on discord to give feedback and get help with the new Llama 3.1 Beta for GPT4All: https://discord.com/invite/4M2QFmTt2k
-
Show HN: Site2pdf
Thanks for taking the time to respond. I was thinking of something local, especially in light of:
Google's Gemini AI caught scanning Google Drive PDF files without permission https://news.ycombinator.com/item?id=40965892 .
Looks like GPT4All[1] and AnythingLLM[2] are worth exploring. There's also the closed-source macOS app RecurseChat[3,4] which appeared on HN a few months ago[5].
[1] https://github.com/nomic-ai/gpt4all
[2] https://github.com/Mintplex-Labs/anything-llm
[3] https://recurse.chat
[4] https://recurse.chat/blog/posts/local-docs
[5] https://news.ycombinator.com/item?id=39532367
- Show HN: I made an app to use local AI as daily driver
-
Ollama Python and JavaScript Libraries
I don’t know if Ollama can do this but https://gpt4all.io/ can.
-
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/
-
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)
What are some alternatives?
flash-attention - Fast and memory-efficient exact attention
ollama - Get up and running with Llama 3.3, DeepSeek-R1, Phi-4, Gemma 2, and other large language models.
koboldcpp - Run GGUF models easily with a KoboldAI UI. One File. Zero Install.
privateGPT - Interact with your documents using the power of GPT, 100% privately, no data leaks [Moved to: https://github.com/zylon-ai/private-gpt]
text-generation-webui - A Gradio web UI for Large Language Models with support for multiple inference backends.
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks
nanoGPT - The simplest, fastest repository for training/finetuning medium-sized GPTs.
llama.cpp - LLM inference in C/C++
alpaca-lora - Instruct-tune LLaMA on consumer hardware
anything-llm - The all-in-one Desktop & Docker AI application with built-in RAG, AI agents, No-code agent builder, and more.
llama - Inference code for Llama models

