SillyTavern
ChatRWKV
SillyTavern | ChatRWKV | |
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75 | 28 | |
677 | 9,291 | |
- | - | |
10.0 | 8.3 | |
12 months ago | 17 days ago | |
JavaScript | Python | |
GNU Affero General Public License v3.0 | Apache License 2.0 |
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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.
SillyTavern
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Help😢
Go to Termix and click Exit. Then go to Termux and code 1. Apk update 2. Apk upgrade 3. git clone https://github.com/Cohee1207/SillyTavern 4. cd SillyTavern 5. Install nodejs 6. Npm install 7. Node server
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Oogabooga and llama.cpp in longer conversations answers take forever.....
If you want the best roleplaying experience, I can only recommend SillyTavern with SillyTavern/SillyTavern-extras. The extras include summarization and ChromaDB, both helping to get longer and more coherent chats.
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koboldcpp-1.33 Ultimate Edition released!
Really? Then we definitely have different experiences (or different ways to interact) with Guanaco. It's been the most unrestricted model I've tried, and I tried them all, but I'm using SillyTavern and the simple-proxy-for-tavern which combined with a little prompting liberates basically any model.
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The best 13B model for rolepay?
Why reinvent the wheel? Just use SillyTavern, ideally with the simple-proxy-for-tavern. That does it all, and more.
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airoboros gpt4 v1.2
I tested this today in an hours-long direct roleplay comparison between q3_K_M quants of TheBloke/airoboros-65B-gpt4-1.2-GGML and TheBloke/guanaco-65B-GGML, using koboldcpp as backend together with simple-proxy-for-tavern and SillyTavern as frontend.
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What are you using for RP?
I'm using SillyTavern frontend and simple-proxy-for-tavern with koboldcpp backend.
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KoboldCPP Updated to Support K-Quants, new bonus CUDA build.
I'm using SillyTavern frontend and simple-proxy-for-tavern with koboldcpp. Not sure which of these has solved the prompt-reprocessing problem, but I no longer have these slowdowns.
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What are your favorite LLMs?
WizardLM 30B V1.0 is not only smarter and follows instructions better than the others, it's even uncensored when used with an uncensoring character card (I use SillyTavern as my GUI/frontend) - more so than any other model I tested. Probably because it follows instructions so well, thus roleplaying an uncensored character properly (and not breaking character or going "as an AI" even once during my tests).
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Potato's brain guide to installing and reopening SillyTavern for Mac
curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.39.3/install.sh | bash export NVM_DIR="$([ -z "${XDG_CONFIG_HOME-}" ] && printf %s "${HOME}/.nvm" || printf %s "${XDG_CONFIG_HOME}/nvm")" [ -s "$NVM_DIR/nvm.sh" ] && \. "$NVM_DIR/nvm.sh" nvm install node git clone -b dev https://github.com/Cohee1207/SillyTavern && cd SillyTavern npm i && node server.js
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I've found a solution to Poe API error
For Android (Termux users): 1. apt update 2. apt upgrade 3. Type "y" to everything and hit enter 4. pkg install git 5. git clone -b dev https://github.com/Cohee1207/SillyTavern 6. cd SillyTavern 7. pkg install nodejs 8. npm install 9. bash start.sh
ChatRWKV
- People who've used RWKV, whats your wishlist for it?
- How the RWKV language model works
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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).
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New model: RWKV-4-Raven-7B-v12-Eng49%-Chn49%-Jpn1%-Other1%-20230530-ctx8192.pth
RWKV models inference: https://github.com/BlinkDL/ChatRWKV (fast CUDA).
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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.
- I created a simple implementation of the RWKV language model (RWKV competes with the dominant Transformers-based approach which is the "T" in GPT)
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[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):
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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
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[D] Totally Open Alternatives to ChatGPT
Please test https://github.com/BlinkDL/ChatRWKV which is a good chatbot despite only trained on the Pile :)
What are some alternatives?
koboldcpp - A simple one-file way to run various GGML and GGUF models with KoboldAI's UI
TavernAI - TavernAI for nerds [Moved to: https://github.com/Cohee1207/SillyTavern]
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
langflow - ⛓️ Langflow is a dynamic graph where each node is an executable unit. Its modular and interactive design fosters rapid experimentation and prototyping, pushing hard on the limits of creativity.
SillyTavern - LLM Frontend for Power Users.
character-editor - Create, edit and convert AI character files for CharacterAI, Pygmalion, Text Generation, KoboldAI and TavernAI
gpt4all - gpt4all: run open-source LLMs anywhere
simple-proxy-for-tavern
KoboldAI
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks
alpaca-lora - Instruct-tune LLaMA on consumer hardware