shady.ai
rwkv.cpp
| shady.ai | rwkv.cpp | |
|---|---|---|
| 1 | 12 | |
| 107 | 1,102 | |
| - | 1.8% | |
| 7.6 | 6.8 | |
| 3 months ago | 24 days ago | |
| Dart | C++ | |
| GNU Affero General Public License v3.0 | MIT License |
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shady.ai
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The Coming of Local LLMs
I’ve got some of their smaller Raven models running locally on my M1 (only 16GB of RAM).
I’m also in the middle of making it user friendly to run these models on all platforms (built with Flutter). First MacOS release will be out before this weekend: https://github.com/BrutalCoding/shady.ai
rwkv.cpp
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Eagle 7B: Soaring past Transformers
There's https://github.com/saharNooby/rwkv.cpp, which related-ish[0] to ggml/llama.cpp
[0]: https://github.com/ggerganov/llama.cpp/issues/846
- People who've used RWKV, whats your wishlist for it?
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The Eleuther AI Mafia
Quantisation thankfully is applicable to RWKV as much as transformers. Most notably in our RWKV.cpp community project: https://github.com/saharNooby/rwkv.cpp
Tooling/Ecosystem is something that I am actively working on as there is still a gap to transformers level of tooling. But i'm glad that there is a noticeable difference!
And yes! experiments are important, to ensure improvements in the architecture. Even if "Linear Transformers" replaces "Transformers". Alternatives should always be explored, to learn from such trade-offs to the benefit of the ecosystem
(This was lightly covered in the podcast, where I share IMO that we should have more research into text based diffusion networks)
- Tiny models for contextually coherent conversations?
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New model: RWKV-4-Raven-7B-v12-Eng49%-Chn49%-Jpn1%-Other1%-20230530-ctx8192.pth
Q8_0 models: only for https://github.com/saharNooby/rwkv.cpp (fast CPU).
- [R] RWKV: Reinventing RNNs for the Transformer Era
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4096 Context length (and beyond)
There's https://github.com/saharNooby/rwkv.cpp which seems to work, and might be compatible with text-generation-webui.
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The Coming of Local LLMs
Also worth checking out https://github.com/saharNooby/rwkv.cpp which is based on Georgi's library and offers support for the RWKV family of models which are Apache-2.0 licensed.
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KoboldCpp - Combining all the various ggml.cpp CPU LLM inference projects with a WebUI and API (formerly llamacpp-for-kobold)
I'm most interested in that last one. I think I heard the RWKV models are very fast, don't need much Ram, and can have huge context tokens, so maybe their 14b can work for me. I wasn't sure how ready for use they were though, but looking more into it, stuff like rwkv.cpp and ChatRWKV and a whole lot of other community projects are mentioned on their github.
- rwkv.cpp: FP16 & INT4 inference on CPU for RWKV language model (r/MachineLearning)
What are some alternatives?
StudentAI - StudentAI is an prompt-less AI chatbot app that uses OpenAI's large language model to help students learn more effectively. StudentAI can answer questions, provide explanations, and even generate creative content. This makes it a powerful tool for students of all ages and levels of learning.
llama.cpp - LLM inference in C/C++
flutter_ci_cd - CI/CD & branching template for flutter apps
RWKV-LM - RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). 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.
more-ane-transformers - Run transformers (incl. LLMs) on the Apple Neural Engine.
ChatRWKV - ChatRWKV is like ChatGPT but powered by RWKV (100% RNN) language model, and open source.
Flutter-AssetsAudioPlayer - Play simultaneously music/audio from assets/network/file directly from Flutter, compatible with android / ios / web / macos, displays notifications
mpt-30B-inference - Run inference on MPT-30B using CPU
verbaflow - Neural Language Model for Go
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM
cformers - SoTA Transformers with C-backend for fast inference on your CPU.
inference - Replace OpenAI GPT with another LLM in your app by changing a single line of code. Xinference gives you the freedom to use any LLM you need. With Xinference, you're empowered to run inference with any open-source language models, speech recognition models, and multimodal models, whether in the cloud, on-premises, or even on your laptop.