alpaca-lora
Instruct-tune LLaMA on consumer hardware (by tloen)
point-alpaca
By pointnetwork
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| alpaca-lora | point-alpaca | |
|---|---|---|
| 107 | 9 | |
| 18,167 | 408 | |
| - | -0.2% | |
| 3.6 | 4.2 | |
| 2 months ago | about 1 year ago | |
| Jupyter Notebook | Python | |
| Apache License 2.0 | - |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.
alpaca-lora
Posts with mentions or reviews of alpaca-lora.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-09-11.
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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
point-alpaca
Posts with mentions or reviews of point-alpaca.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-03-21.
- point-alpaca
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Pygmalion releases two new LLaMA based models: Pygmalion 7B and the roleplay oriented Metharme 7B. These are major improvements over the old Pygmalion models.
How does this perform compared to something like https://github.com/pointnetwork/point-alpaca?
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What AI models do you want me to test and judge with GPT-4? Taking suggestions from the community!
How does https://github.com/pointnetwork/point-alpaca compare? I was surprised how well the demo performed.
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Is this good idea to buy more rtx 3090?
I don't think it's worth it. The smaller models are powerful enough for most purposes. Did you try Point Alpaca? https://github.com/pointnetwork/point-alpaca
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What's the current "Best" LLaMA LoRA? or moreover what would be a good benchmark to test these against. (HF links incl in post)
It's not a LoRA, but this is the best I've tried: https://github.com/pointnetwork/point-alpaca It requires a GPU.
- Alpaca recreation without LORA ( released as a diff. )
- Goodbye Alpaca
- [D] Totally Open Alternatives to ChatGPT
What are some alternatives?
When comparing alpaca-lora and point-alpaca you can also consider the following projects:
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
stanford_alpaca - Code and documentation to train Stanford's Alpaca models, and generate the data.
qlora - QLoRA: Efficient Finetuning of Quantized LLMs
petals - 🌸 Run LLMs at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading
llama.cpp - LLM inference in C/C++
awesome-totally-open-chatgpt - A list of totally open alternatives to ChatGPT
gpt4all - gpt4all: run open-source LLMs anywhere
llama - Inference code for Llama models
ggml - Tensor library for machine learning
RWKV-LM - RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding.
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM
LoRA - Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"
alpaca-lora vs text-generation-webui
point-alpaca vs stanford_alpaca
alpaca-lora vs qlora
point-alpaca vs petals
alpaca-lora vs llama.cpp
point-alpaca vs awesome-totally-open-chatgpt
alpaca-lora vs gpt4all
alpaca-lora vs llama
alpaca-lora vs ggml
alpaca-lora vs RWKV-LM
alpaca-lora vs alpaca.cpp
alpaca-lora vs LoRA