LLaMA-LoRA-Tuner VS CodeCapypara

Compare LLaMA-LoRA-Tuner vs CodeCapypara and see what are their differences.

LLaMA-LoRA-Tuner

UI tool for fine-tuning and testing your own LoRA models base on LLaMA, GPT-J and more. One-click run on Google Colab. + A Gradio ChatGPT-like Chat UI to demonstrate your language models. (by zetavg)

CodeCapypara

[Moved to: https://github.com/FSoft-AI4Code/CodeCapybara] (by AI4Code-Research)
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LLaMA-LoRA-Tuner CodeCapypara
6 1
426 94
- -
7.9 10.0
12 months ago about 1 year ago
Python 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.
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LLaMA-LoRA-Tuner

Posts with mentions or reviews of LLaMA-LoRA-Tuner. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-05-25.

CodeCapypara

Posts with mentions or reviews of CodeCapypara. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-04-24.

What are some alternatives?

When comparing LLaMA-LoRA-Tuner and CodeCapypara you can also consider the following projects:

CodeCapybara - Open-source Self-Instruction Tuning Code LLM

AlpacaDataCleaned - Alpaca dataset from Stanford, cleaned and curated

example-scalping - A working example algorithm for scalping strategy trading multiple stocks concurrently using python asyncio

BELLE - BELLE: Be Everyone's Large Language model Engine(开源中文对话大模型)

example-hftish - Example Order Book Imbalance Algorithm

lora - Train Large Language Models (LLM) using LoRA

ExpertLLaMA - An opensource ChatBot built with ExpertPrompting which achieves 96% of ChatGPT's capability.

koboldcpp - A simple one-file way to run various GGML and GGUF models with KoboldAI's UI

self-instruct - Aligning pretrained language models with instruction data generated by themselves.

simple-llm-finetuner - Simple UI for LLM Model Finetuning

safe-rlhf - Safe RLHF: Constrained Value Alignment via Safe Reinforcement Learning from Human Feedback