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Main Github
Hugging Face Integration
Community Discord
  • RWKV Language Model
  • Getting Started
    • How to Experience RWKV
    • RWKV Decoding Parameters
    • Integrate with your application
    • Frequently Asked Questions
  • RWKV Prompting
    • Prompting Format Guidelines
    • Chat Prompt Examples
    • Completion Prompt Examples
  • Advanced
    • RWKV-LM Pre-training Tutorial
    • RWKV-FLA User Guide
    • Fine-tuning
    • Preparing The Training Datasets
    • RWKV Training Environment
    • RWKV Architecture History
    • RWKV pip Usage Guide
  • Inference Tutorials
    • llama.cpp Inference
    • Ollama Inference
    • Silly Tavern Inference
    • Text Generation WebUI Inference
    • KoboldCpp Inference
    • Ai00 Inference
  • Fine Tune Tutorials
    • State Tuning Tutorial
    • LoRA Fine-Tuning Tutorial
    • PiSSA Fine-Tuning Tutorial
    • DiSHA Fine-Tuning Tutorial
    • FAQ about Fine-Tuning
  • Community
    • Code Of Conduct
    • Contributing to RWKV
    • Various RWKV related links

RWKV raven avartar

RWKV Language Model

RWKV (pronounced RWaKuV) is an RNN with GPT-level large language model (LLM) performance that can be trained directly like a GPT Transformer (parallelizable).

RWKV combines the best features of RNN and Transformer: excellent performance, constant memory usage, constant inference generation speed, "infinite" context length, and free sentence embeddings. It is also 100% free of self-attention mechanisms.

The RWKV project was initially proposed by Bo Peng (Blink_DL), and as the project gained attention, it gradually developed into an open-source community.

On September 20, 2023, the RWKV open-source project officially joined the Linux Foundation. Today, the RWKV project is an open-source non-profit organization under the Linux Foundation, with some computing power previously supported by sponsors.

  • Discord Forum
  • HF Gradio-1 | RWKV7-G1 2.9B
  • HF Gradio-2 | RWKV7-G1

RWKV Architecture and Papers

RWKV-7 (Goose) is the latest version of the RWKV architecture. The paper was co-authored by Bo Peng and the RWKV community, published on March 18, 2025.

  • RWKV-7 Paper: "RWKV-7 Goose with Expressive Dynamic State Evolution"
  • Paper Link: arXiv:2503.14456

RWKV-7 adopts Dynamic State Evolution, surpassing the fundamental limitations of the TC0 expressive power of the attention/linear attention paradigm.

Click to view RWKV-7 Architecture Diagram

RWKV-7-architecture

RWKV 5/6 (Eagle/Finch) architectures have several improvements based on the RWKV-4 architecture. Therefore, these two architectures are published in the same paper.

  • RWKV 5/6 Paper: "Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence"
  • Paper Link: arXiv:2404.05892

RWKV-4 is the first official version of the RWKV model. The paper was co-authored by Bo Peng and the RWKV community and was first published on May 22, 2023. In October of the same year, the RWKV-4 architecture paper was accepted by EMNLP 2023.

  • RWKV-4 Paper: "RWKV: Reinventing RNNs for the Transformer Era"
  • Paper Link: arXiv:2305.13048

RWKV Model Version Status

RWKV has released open-source models of various parameter scales for each architecture version.

VersionRWKV-V4RWKV-v5-EagleRWKV-v6-FinchRWKV-v7-GooseRWKV-v7-G1
PaperPublishedPublishedPublishedPublishedPublished
Overall StatusEOLEOLStableStableIn Progress
0.4B ModelReleasedReleasedNo PlanReleasedReleased
1.5B ModelReleasedReleasedReleasedReleasedReleased
3B ModelReleasedReleasedReleasedReleasedReleased
7B ModelReleasedReleasedReleasedNo Plan⚙️ In Training
14B ModelReleasedNo PlanReleasedNo Plan📅 Planned

Which RWKV Models Should I Use?

Here's the English translation:

Tips

Due to performance issues caused by outdated architectures, all RWKV-5, RWKV-4 series models (Raven / World / Pile ...) and earlier RWKV versions have reached end-of-life, with existing models serving only as archives.

  • Models with 7B parameters and below: Please use the RWKV7-G1 2.9B model. Thanks to the latest architectural design and the newest World v3.5 dataset (totaling 5.16T tokens), the RWKV7-G1 model has strong reasoning, coding, and mathematical capabilities, performing better than previous models across all tasks.

  • Models with more than 7B parameters: Since RWKV7-G1 7B and larger models are currently in training, we recommend using the RWKV-6-World-14B-V2.1 model.

Tips

After the RWKV7-G1 7B/14B models complete training, they will completely replace the existing RWKV-6-World 7B/14B models.

Differences Between RWKV and Transformer

  • Advantages

    • Lower resource usage during runtime and training (VRAM, CPU, GPU, etc.).
    • 10 to 100 times lower computational requirements compared to Transformers with larger contexts.
    • Supports linear scaling to any context length (Transformers scale quadratically).
    • Performs as well as Transformer architectures in terms of answer quality and generalization ability.
    • RWKV models' training data includes languages other than English (e.g., Chinese, Japanese, etc.), offering better multilingual capabilities than most existing open-source models.
  • Disadvantages

    • RWKV base models are very sensitive to the format of prompts, and the format of prompts significantly affects the generation results.
    • Due to architectural design, RWKV models are weaker on tasks requiring lookback/review, and we are working on various optimizations to address this issue.

Basic Terminology of the RWKV Community

ConceptDescription
RWKVThe model architecture itself, training code available here
stateRWKV is a variant of RNN architecture, state is the hidden state that RWKV passes across time steps during inference, used to retain historical context information
ChatRWKVOfficial chatbot for RWKV (similar to ChatGPT, but based on RWKV), code available here
RWKV-4/5/6/7Different architectural versions of RWKV. Note that the latest RWKV-7 series models are recommended
RWKV WorldBase RWKV models trained on data from over 100 languages worldwide. These models cover a broader and more diverse dataset, including training data from over 100 languages, as well as some instruction tuning
RavenOfficial fine-tuned version of the RWKV-4 base model, including instruction training. However, since the RWKV-4 series has been discontinued, continued use is not recommended
RWKV ABC/MIDIRWKV music models based on ABC/MIDI format
RWKV CHNtuned / one-state-chat / role_play / novel ...Fine-tuned models provided by the RWKV community, optimized for specific tasks or data types. Please prioritize using fine-tuned models from the RWKV-7 series
RWKV7-G1 (Goose One)Base model trained on RWKV-7 architecture and World v3.5 dataset, supports reasoning/thinking (Think), with stronger performance

RWKV Model Naming Rules

RWKV models typically have two naming conventions:

  • RWKV-6-World-3B-v2.1-20240208-ctx4096.pth
  • RWKV-x070-World-1.5B-v3-20250127-ctx4096.pth

The meaning of each field in the model name:

FieldMeaning
RWKVModel name
6 / 070RWKV model architecture, recommended to use RWKV-7 models
WorldModel type, World indicates RWKV models trained with global languages, thus supporting multilingual tasks
3B / 1.5BModel parameter scale, "B" stands for "Billions"
v2.1 / v3Model training dataset version, v2.1 ≈ 1.1T , v3 ≈ 2.5T
20240208 / 20250127Model release date
ctx4096Pre-trained context length
.pthRWKV model file format, also supports .gguf and .safetensors etc.

Who sponsors the compute for RWKV?

RWKV is made possible, as an Open Source project, thanks to the large amount of GPU compute and researchers time contributions from

Without their invaluable support, we would not have been able to develop the core RWKV foundation models that you see today.


In addition, we would like to thank

  • alpin @ pygmalionAI
  • AutoMeta @ AlignmentLab
  • FeatherlessAI
  • Various other folks who donated slices of GPU time / preferred not to be named

For helping with GPU time, on smaller experiments, finetunes, and various models. Especially for those models that never get publically released in failed runs.

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Contributors: luoqiqi, manjuan, 漫卷
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