RWKV-LM VS nanoGPT

Compare RWKV-LM vs nanoGPT and see what are their differences.

RWKV-LM

RWKV (pronounced RwaKuv) is an RNN with great LLM performance, which can also be directly trained like a GPT transformer (parallelizable). We are at RWKV-7 "Goose". So it's combining the best of RNN and transformer - great performance, linear time, constant space (no kv-cache), fast training, infinite ctx_len, and free sentence embedding. (by BlinkDL)

nanoGPT

The simplest, fastest repository for training/finetuning medium-sized GPTs. (by karpathy)
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RWKV-LM nanoGPT
85 76
13,137 39,408
2.2% 3.1%
9.1 3.6
12 days ago 2 months ago
Python Python
Apache License 2.0 MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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

Posts with mentions or reviews of RWKV-LM. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2025-02-14.
  • Ask HN: Is anybody building an alternative transformer?
    3 projects | news.ycombinator.com | 14 Feb 2025
    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?
    1 project | news.ycombinator.com | 25 Dec 2023
    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?
    9 projects | /r/LocalLLaMA | 9 Dec 2023
  • Paving the way to efficient architectures: StripedHyena-7B
    1 project | news.ycombinator.com | 8 Dec 2023
  • Understanding Deep Learning
    1 project | news.ycombinator.com | 26 Nov 2023
    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
    3 projects | news.ycombinator.com | 19 Sep 2023
    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
    14 projects | /r/ChatGPT | 30 Jun 2023
    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"
    1 project | /r/singularity | 26 Jun 2023
    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
    3 projects | news.ycombinator.com | 17 Jun 2023
    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
    1 project | news.ycombinator.com | 16 Jun 2023
    > 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.

nanoGPT

Posts with mentions or reviews of nanoGPT. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2025-01-29.
  • A minimal PyTorch implementation for training your own small LLM from scratch
    8 projects | news.ycombinator.com | 29 Jan 2025
    The one you linked to is based on Karpathy's tutorial: https://www.youtube.com/watch?v=kCc8FmEb1nY, except changing to train on tinystories in stead of Shakespeare. smolGPT also looks inspired by nanoGPT, also from Karpathy: https://github.com/karpathy/nanoGPT/blob/master/train.py
  • Probably Pay Attention to Tokenizers
    1 project | news.ycombinator.com | 24 Oct 2024
    You would have to train the new model from scratch since it would be all new token embeddings with whatever character encoding scheme you come up with. It would probably make sense to train the vanilla gpt from scratch with the same total embeddings size as your control. I would start with https://github.com/karpathy/nanoGPT as a baseline since you can train a toy (GPT2 sized) llm in a couple days on an a100 which are pretty easy to come by.
  • Tiny Shakespeare, of the good old char-RNN fame
    1 project | news.ycombinator.com | 4 Sep 2024
  • FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-Precision
    5 projects | news.ycombinator.com | 11 Jul 2024
    There are a bunch of good answers, but I wanted to succinctly say "practically, quite a bit". Here's a good little rabbit-hole example:

    > https://github.com/karpathy/nanoGPT/blob/master/model.py#L45

    Karpathy's nanoGPT calling flash attention by checking if torch.nn.functional.scaled_dot_product_attention exists

    > https://pytorch.org/docs/stable/generated/torch.nn.functiona...

    Looking at the docs, in reality, most of the time you want this to call out to FA2 which optimizes the kernals on the device to split ops on the Softmax of the triangular matrix as well as reduce moving unnecessary batches of floating point numbers back and forth from the GPU to the CPU.

    > https://arxiv.org/pdf/2307.08691

    The paper for FA2 almost entirely considers itself through the hardware it's running on.

  • NanoGPT: The simplest, fastest repository for training medium-sized GPTs
    2 projects | news.ycombinator.com | 10 Jun 2024
  • Show HN: Predictive Text Using Only 13KB of JavaScript. No LLM
    3 projects | news.ycombinator.com | 1 Mar 2024
    Nice work! I built something similar years ago and I did compile the probabilities based on a corpus of text (public domain books) in an attempt to produce writing in the style of various authors. The results were actually quite similar to the output of nanoGPT[0]. It was very unoptimized and everything was kept in memory. I also knew nothing about embeddings at the time and only a little about NLP techniques that would certainly have helped. Using a graph database would have probably been better than the datastructure I came up with at the time. You should look into stuff like Datalog, Tries[1], and N-Triples[2] for more inspiration.

    You're idea of splitting the probabilities based on whether you're starting the sentence or finishing it is interesting but you might be able to benefit from an approach that creates a "window" of text you can use for lookup, using an LCS[3] algorithm could do that. There's probably a lot of optimization you could do based on the probabilities of different sequences, I think this was the fundamental thing I was exploring in my project.

    Seeing this has inspired me further to consider working on that project again at some point.

    [0] https://github.com/karpathy/nanoGPT

    [1] https://en.wikipedia.org/wiki/Trie

    [2] https://en.wikipedia.org/wiki/N-Triples

    [3] https://en.wikipedia.org/wiki/Longest_common_subsequence

  • LLMs Learn to Be "Generative"
    1 project | news.ycombinator.com | 4 Feb 2024
    where x1 denotes the 1st token, x2 denotes the 2nd token and so on, respectively.

    I understand the conditional terms p(x_n|...) where we use cross-entropy to calculate their losses. However, I'm unsure about the probability of the very first token p(x1). How is it calculated? Is it in some configurations of the training process, or in the model architecture, or in the loss function?

    IMHO, if the model doesn't learn p(x1) properly, the entire formula for Bayes' rule cannot be completed, and we can't refer to LLMs as "truly generative". Am I missing something here?

    I asked the same question on nanoGPT repo: https://github.com/karpathy/nanoGPT/issues/432, but I haven't found the answer I'm looking for yet. Could someone please enlighten me.

  • A simulation of me: fine-tuning an LLM on 240k text messages
    2 projects | news.ycombinator.com | 4 Jan 2024
    This repo, albeit "old" in regards to how much progress there's been in LLMs, has great simple tutorials right there eg. fine-tuning GPT2 with Shakespeare: https://github.com/karpathy/nanoGPT
  • Ask HN: Is it feasible to train my own LLM?
    3 projects | news.ycombinator.com | 2 Jan 2024
    For training from scratch, maybe a small model like https://github.com/karpathy/nanoGPT or tinyllama. Perhaps with quantization.
  • Writing a C compiler in 500 lines of Python
    4 projects | news.ycombinator.com | 4 Sep 2023
    It does remind me of a project [1] Andrej Karpathy did, writing a neural network and training code in ~600 lines (although networks have easier logic to code than a compiler).

    [1] https://github.com/karpathy/nanoGPT

What are some alternatives?

When comparing RWKV-LM and nanoGPT you can also consider the following projects:

flash-attention - Fast and memory-efficient exact attention

minGPT - A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training

koboldcpp - Run GGUF models easily with a KoboldAI UI. One File. Zero Install.

ChatGPT - 🔮 ChatGPT Desktop Application (Mac, Windows and Linux)

text-generation-webui - A Gradio web UI for Large Language Models with support for multiple inference backends.

PaLM-rlhf-pytorch - Implementation of RLHF (Reinforcement Learning with Human Feedback) on top of the PaLM architecture. Basically ChatGPT but with PaLM

gpt4all - GPT4All: Run Local LLMs on Any Device. Open-source and available for commercial use.

nn-zero-to-hero - Neural Networks: Zero to Hero

alpaca-lora - Instruct-tune LLaMA on consumer hardware

gpt_index - LlamaIndex (GPT Index) is a project that provides a central interface to connect your LLM's with external data. [Moved to: https://github.com/jerryjliu/llama_index]

llama - Inference code for Llama models

awesome-chatgpt-prompts - This repo includes ChatGPT prompt curation to use ChatGPT and other LLM tools better.

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