alpaca-lora
lamini
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alpaca-lora | lamini | |
---|---|---|
107 | 9 | |
18,167 | 2,412 | |
- | 1.4% | |
3.6 | 7.3 | |
2 months ago | 18 days ago | |
Jupyter Notebook | Python | |
Apache License 2.0 | Apache License 2.0 |
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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
lamini
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[P] Free and Fast LLM Finetuning
Github Repo: https://github.com/lamini-ai/lamini
- Free and Fast LLM Finetuning
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[P] Lamini rapidly achieves ChatGPT performance with an LLM Engine
The data pipeline here https://github.com/lamini-ai/lamini uses a seed dataset from self-instruct (Apache 2 license), and edited models from Pythia (Apache 2) and Dolly (Apache 2). We release our code and data under a CC-BY 4.0 license.
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Launch Lamini: The LLM Engine for Rapidly Customizing Models as Good as ChatGPT
Today, you can try out our hosted data generator for training your own LLMs, weights and all, without spinning up any GPUs, in just a few lines of code from the Lamini library. https://github.com/lamini-ai/lamini/
You can play with an open-source LLM, trained on generated data using Lamini. https://huggingface.co/spaces/lamini/instruct-playground
Sign up for early access to the training module that took the generated data and trained it into this LLM, including enterprise features like virtual private cloud (VPC) deployments. https://lamini.ai/contact
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Seeking Language Project to Join
example: https://github.com/lamini-ai/lamini
What are some alternatives?
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
qlora - QLoRA: Efficient Finetuning of Quantized LLMs
peft - 🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
llama.cpp - LLM inference in C/C++
flix - The Flix Programming Language
gpt4all - gpt4all: run open-source LLMs anywhere
otterkit - A free and open source Standard COBOL compiler for 64-bit environments
llama - Inference code for Llama models
lamini-sql - SQL autocomplete data
ggml - Tensor library for machine learning
Wave - A cool programming language.