ChatRWKV
rwkv.cpp
ChatRWKV | rwkv.cpp | |
---|---|---|
28 | 12 | |
9,291 | 1,102 | |
- | 1.8% | |
8.3 | 6.8 | |
11 days ago | 22 days ago | |
Python | C++ | |
Apache License 2.0 | MIT License |
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.
ChatRWKV
- People who've used RWKV, whats your wishlist for it?
- How the RWKV language model works
-
Questions about memory, tree-of-thought, planning
Most LLMs actually do a decent job out of the box if you ask them for step by step instructions. Tree of tough is one way to improve the results, reflexion is another that can be used separate or additionally. The downside is that most models will run quickly into their token limit (around 2k for most). However the new SuperHot models can handle up to 8k and then there are the RMVK-Raven models, they are RNNs and not transformers like all the other LLMs and can theoretically handle infinite context lengths (but they loose "focus" after a while).
-
New model: RWKV-4-Raven-7B-v12-Eng49%-Chn49%-Jpn1%-Other1%-20230530-ctx8192.pth
RWKV models inference: https://github.com/BlinkDL/ChatRWKV (fast CUDA).
-
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.
- I created a simple implementation of the RWKV language model (RWKV competes with the dominant Transformers-based approach which is the "T" in GPT)
-
[P] Raven 7B & 14B 🐦(RWKV finetuned on Alpaca+CodeAlpaca+Guanaco) and Gradio Demo for Raven 7B
You can use ChatRWKV v2 (https://github.com/BlinkDL/ChatRWKV) to run Raven🐦 (compatible with vanilla RWKV):
-
What's the current state of actually free and open source LLMs?
I feel compelled to summon /u/bo_peng here and to mention his work on RWKV. (See https://github.com/BlinkDL/ChatRWKV and related repos.)
- Try Google's Bard
-
[D] Totally Open Alternatives to ChatGPT
Please test https://github.com/BlinkDL/ChatRWKV which is a good chatbot despite only trained on the Pile :)
rwkv.cpp
-
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?
-
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?
-
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
-
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.
-
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.
-
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?
koboldcpp - A simple one-file way to run various GGML and GGUF models with KoboldAI's UI
llama.cpp - LLM inference in C/C++
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
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.
SillyTavern - LLM Frontend for Power Users.
mpt-30B-inference - Run inference on MPT-30B using CPU
SillyTavern - LLM Frontend for Power Users. [Moved to: https://github.com/SillyTavern/SillyTavern]
verbaflow - Neural Language Model for Go
gpt4all - gpt4all: run open-source LLMs anywhere
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM
KoboldAI
cformers - SoTA Transformers with C-backend for fast inference on your CPU.