catai
alpaca-lora
catai | alpaca-lora | |
---|---|---|
7 | 107 | |
408 | 18,217 | |
1.7% | - | |
8.6 | 3.6 | |
3 months ago | 2 months ago | |
TypeScript | Jupyter Notebook | |
MIT License | Apache License 2.0 |
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catai
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Are you sure you are focusing on the right things? (venting)
The easiest tool I found is CatAI: https://github.com/ido-pluto/catai You just type 3 npm commands and THATS IT! You have your own Chat Web UI on your computer without hundrets of settings
- How to use CatAI to apologize to your boss
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Wizard-Vicuna-13B-Uncensored
I am a noob. I saw your comment on github and another post here. I am confused about what has changed and what us users have to do. Do we have to update llama.cpp and redownload all the models(I am using something called catai instead of the webui, i think it also uses llama.cpp)? How do we know which versions of the models are compatible with which vesions of llama.cpp?
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Google removes the waitlist on Bard today and will be available in 180 more countries
https://github.com/ggerganov/llama.cpp https://github.com/oobabooga/text-generation-webui https://github.com/mlc-ai/mlc-llm https://github.com/cocktailpeanut/dalai https://github.com/ido-pluto/catai (this is super easy to install but it doesnt provide an api or have integration with langchain)
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GPT For All 13B (/GPT4All-13B-snoozy-GPTQ) is Completely Uncensored, a great model
Pretty simple using catai.
- How to run something like chatgpt, locally?
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How to install Wizard-Vicuna
You can check out the original GitHub project here
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 "á¾¾" 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?
alpaca-electron - The simplest way to run Alpaca (and other LLaMA-based local LLMs) on your own computer
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
HyunGPT - chatbot thing
qlora - QLoRA: Efficient Finetuning of Quantized LLMs
AutoGPTQ - An easy-to-use LLMs quantization package with user-friendly apis, based on GPTQ algorithm.
llama.cpp - LLM inference in C/C++
gpt4all - gpt4all: run open-source LLMs anywhere
dalai - The simplest way to run LLaMA on your local machine
llama - Inference code for Llama models
mlc-llm - Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.
ggml - Tensor library for machine learning