gpt-engineer
alpaca-lora
gpt-engineer | alpaca-lora | |
---|---|---|
45 | 107 | |
51,008 | 18,312 | |
0.9% | - | |
9.9 | 3.6 | |
6 days ago | 4 months ago | |
Python | Jupyter Notebook | |
MIT License | Apache License 2.0 |
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.
gpt-engineer
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Ask HN: Who is hiring? (June 2024)
Lovable (https://lovable.dev), creators of GPT Engineer (https://gptengineer.app) | ONSITE+HYBRID | London / Stockholm | Full-Time | Founding Engineers
We're a small team of serial (ex-YC) founders, CTOs, designers & IOI gold medalists set on being the first to make autonomous code generation work.
Our CLI tool has 50k GitHub stars (https://github.com/gpt-engineer-org/gpt-engineer) and our SaaS product is in closed beta with 25k on the waitlist. You'd be one of the first employees and have a big impact in shaping our product and company.
Building an AI Software Engineer is a hard problem. We believe that hiring the brightest and most ambitious minds in Europe is the way we make this work. We care deeply about building a great product. We want to work with people who are as ambitious and motivated about shipping fast as we are.
In particular, we're looking for:
- Product engineers: Product-minded full stack engineers to work on our core product.
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π 7 AI Tools to Improve your productivity: A Deep Dive πͺβ¨
2οΈβ£ GPT-engineer π§ͺ
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Ask HN: Will AI take no-code to the next level?
you should check out this:
https://github.com/AntonOsika/gpt-engineer
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Ask HN: How can ChatGPT be effectively utilized in the work
3. https://github.com/AntonOsika/gpt-engineer
- GPT Engineer
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[P] Looking for a new problem to solve with LLMs and AI.
Framework to Generate HQ code (I'm a mod at gpt-engineer)
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GPT-Prompt-Engineer
BTW, GPT-Engineer is openly collecting all of your data: user prompts and other metadata. And they were even defending it until they received some strong responses from the community: https://github.com/AntonOsika/gpt-engineer/issues/415 They now explicitly ask for consent regarding user data, but can we really trust their motives?
- Ai create entire project
- GPT-Engineer β Specify what you want it to build, the AI builds it
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I'm creating a GPT3.5-based javascript Game Engine
You might be interested in gpt-engineer https://github.com/AntonOsika/gpt-engineer
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 "αΎΎ" 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?
gpt-pilot - The first real AI developer
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
aider - aider is AI pair programming in your terminal
qlora - QLoRA: Efficient Finetuning of Quantized LLMs
MetaGPT - π The Multi-Agent Framework: First AI Software Company, Towards Natural Language Programming
llama.cpp - LLM inference in C/C++
developer - the first library to let you embed a developer agent in your own app!
gpt4all - gpt4all: run open-source LLMs anywhere
hive-metastore - Apache Hive Metastore as a Standalone server in Docker
llama - Inference code for Llama models
StanfordQuadruped
ggml - Tensor library for machine learning