rwkv.cpp
cformers
| rwkv.cpp | cformers | |
|---|---|---|
| 12 | 4 | |
| 1,111 | 314 | |
| 2.6% | 0.3% | |
| 6.8 | 6.7 | |
| 29 days ago | 5 months ago | |
| C++ | C | |
| MIT License | MIT License |
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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)
cformers
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[P] rwkv.cpp: FP16 & INT4 inference on CPU for RWKV language model
it's a combination of things, and removing python from the loop isn't essential to achieving most of these performance gains. the main trick is quantizing the weights and compiling the model. concrete example that builds on top of ggml with python APIs: https://github.com/NolanoOrg/cformers
- Cformers 🚀 - "Transformers with a C-backend for lightning-fast CPU inference". | Nolano
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FauxPilot – an open-source GitHub Copilot server
We will add quantized CodeGen for fast inference on CPUs up on cformers (https://github.com/NolanoOrg/cformers/) by later today.
What are some alternatives?
llama.cpp - LLM inference in C/C++
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.
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM
ChatRWKV - ChatRWKV is like ChatGPT but powered by RWKV (100% RNN) language model, and open source.
mpt-30B-inference - Run inference on MPT-30B using CPU
CodeGen - CodeGen is a family of open-source model for program synthesis. Trained on TPU-v4. Competitive with OpenAI Codex.
verbaflow - Neural Language Model for Go
llm - An ecosystem of Rust libraries for working with large language models
gpt4all.cpp - Locally run an Assistant-Tuned Chat-Style LLM