alpaca-7b-truss
alpaca-lora
alpaca-7b-truss | alpaca-lora | |
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2 | 107 | |
317 | 18,238 | |
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6.0 | 3.6 | |
11 months ago | 3 months ago | |
Python | Jupyter Notebook | |
- | Apache License 2.0 |
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alpaca-7b-truss
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[Project] ChatLLaMA - A ChatGPT style chatbot for Facebook's LLaMA
If you want deploy your own instance is the model powering the chatbot and build something similar we've open sourced the Truss here: https://github.com/basetenlabs/alpaca-7b-truss
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Show HN: ChatLLaMA – A ChatGPT style chatbot for Facebook's LLaMA
ChatLLaMA is an experimental chatbot interface for interacting with variants of Facebook's LLaMA. Currently, we support the 7 billion parameter variant that was fine-tuned on the Alpaca dataset. This early versions isn't as conversational as we'd like, but over the next week or so, we're planning on adding support for the 30 billion parameter variant, another variant fine-tuned on LAION's OpenAssistant dataset and more as we explore what this model is capable of.
If you want deploy your own instance is the model powering the chatbot and build something similar we've open sourced the Truss here: https://github.com/basetenlabs/alpaca-7b-truss
We'd love to hear any feedback you have. You can reach me on Twitter @aaronrelph or Abu (the engineer behind this) @aqaderb.
Disclaimer: We both work at Baseten. This was a weekend project. Not trying to shill anything; just want to build and share cool stuff.
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?
hh-rlhf - Human preference data for "Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback"
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
chatllama - ChatLLaMA 📢 Open source implementation for LLaMA-based ChatGPT runnable in a single GPU. 15x faster training process than ChatGPT
qlora - QLoRA: Efficient Finetuning of Quantized LLMs
stanford_alpaca - Code and documentation to train Stanford's Alpaca models, and generate the data.
llama.cpp - LLM inference in C/C++
nebuly - The user analytics platform for LLMs
gpt4all - gpt4all: run open-source LLMs anywhere
LLM-As-Chatbot - LLM as a Chatbot Service
llama - Inference code for Llama models
ggml - Tensor library for machine learning