ChatRWKV

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ChatRWKV is an AI Agents & Automation tool that provides an open-source chatbot powered by the RWKV (100% RNN) language model. It aims to match the quality and scaling of transformers while being faster and saving VRAM.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is ChatRWKV?

ChatRWKV is an open-source chatbot project that leverages the RWKV language model, a 100% RNN architecture designed to compete with transformers in terms of quality and scaling. A key differentiator is its efficiency, offering faster performance and reduced VRAM consumption. The project provides various resources, including code for building inference engines, API demos for chat and world models, and links to community projects and pre-trained weights. It supports different strategies for inference, including CUDA kernels for speed, and offers options for running models with lower VRAM requirements, such as 3GB for RWKV 14B with INT8. The tool is designed for developers and researchers interested in exploring and implementing RNN-based language models.

Best used for

Ideal for developers and data scientists who need to build custom chatbots, implement efficient language models, and optimize AI inference. Especially valuable for those looking to leverage RNN architectures that offer performance comparable to transformers while consuming less VRAM.

Common actions

build chatbots

implement language models

optimize AI inference

experiment with RNNs

"AI Agents"github copilotopen-sourceautomated workflowface swappingdeepfakecollaborationlow-code/no-codeworkflows

Capabilities

Key features

100% RNN language model
Matches transformer quality
Faster inference
Saves VRAM
Open-source code
API demos
CUDA kernel support

Target Audience

developerdata scientiststartup founder

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What is the primary advantage of ChatRWKV over transformer-based models?

ChatRWKV, powered by the RWKV (100% RNN) language model, offers comparable quality and scaling to transformers while providing faster inference and significant VRAM savings. This makes it a more efficient option for certain applications and hardware constraints.

Can ChatRWKV run on systems with limited VRAM?

Yes, ChatRWKV is designed to be VRAM efficient. For example, it's possible to run the RWKV 14B model with as little as 3GB of VRAM by utilizing strategies like INT8 quantization and CUDA kernels for optimized performance.

Where can I find pre-trained RWKV models for use with ChatRWKV?

Pre-trained RWKV weights, including raw cutting-edge models and HF-compatible versions, are available on Hugging Face. The project also provides links to specific Raven-series models tuned for chat, which are noted for their strong performance.

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