SillyTavern
alpaca-lora
SillyTavern | alpaca-lora | |
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75 | 107 | |
677 | 18,238 | |
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10.0 | 3.6 | |
12 months ago | 3 months ago | |
JavaScript | Jupyter Notebook | |
GNU Affero General Public License v3.0 | Apache License 2.0 |
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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
alpaca-lora
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How to deal with loss for SFT for CausalLM
Here is a example: https://github.com/tloen/alpaca-lora/blob/main/finetune.py
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How to Finetune Llama 2: A Beginner's Guide
In this blog post, I want to make it as simple as possible to fine-tune the LLaMA 2 - 7B model, using as little code as possible. We will be using the Alpaca Lora Training script, which automates the process of fine-tuning the model and for GPU we will be using Beam.
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Fine-tuning LLMs with LoRA: A Gentle Introduction
Implement the code in Llama LoRA repo in a script we can run locally
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Newbie here - trying to install a Alpaca Lora and hitting an error
Hi all - relatively new to GitHub / programming in general, and I wanted to try to set up Alpaca Lora locally. Following the guide here: https://github.com/tloen/alpaca-lora
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A simple repo for fine-tuning LLMs with both GPTQ and bitsandbytes quantization. Also supports ExLlama for inference for the best speed.
Follow up the popular work of u/tloen alpaca-lora, I wrapped the setup of alpaca_lora_4bit to add support for GPTQ training in form of installable pip packages. You can perform training and inference with multiple quantizations method to compare the results.
- FLaNK Stack Weekly for 20 June 2023
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Converting to GGML?
If instead you want to apply a LoRa to a pytorch model, a lot of people use this script to apply to LoRa to the 16 bit model and then quantize it with a GPTQ program afterwards https://github.com/tloen/alpaca-lora/blob/main/export_hf_checkpoint.py
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Simple LLM Watermarking - Open Lllama 3b LORA
There are a few papers on watermarking LLM output, but from what I have seen they all use complex methods of detection to allow the watermark to go unseen by the end user, only to be detected by algorithm. I believe that a more overt system of watermarking might also be beneficial. One simple method that I have tried is character substitution. For this model, I LORA finetuned openlm-research/open_llama_3b on the alpaca_data_cleaned_archive.json dataset from https://github.com/tloen/alpaca-lora/ modified by replacing all instances of the "." character in the outputs with a "ι" The results are pretty good, with the correct the correct substitutions being generated by the model in most cases. It doesn't always work, but this was only a LORA training and for two epochs of 400 steps each, and 100% substitution isn't really required.
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text-generation-webui's "Train Only After" option
I am kind of new to finetuning LLM's and am not able to understand what this option exactly refers to. I guess it has the same meaning as the "train_on_inputs" parameter of alpacalora though.
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Learning sources on working with local LLMs
Read the paper and also: https://github.com/tloen/alpaca-lora
What are some alternatives?
koboldcpp - A simple one-file way to run various GGML and GGUF models with KoboldAI's UI
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
TavernAI - TavernAI for nerds [Moved to: https://github.com/Cohee1207/SillyTavern]
qlora - QLoRA: Efficient Finetuning of Quantized LLMs
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.
llama.cpp - LLM inference in C/C++
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
llama - Inference code for Llama models
ChatRWKV - ChatRWKV is like ChatGPT but powered by RWKV (100% RNN) language model, and open source.
ggml - Tensor library for machine learning