xTuring
FinGPT
| xTuring | FinGPT | |
|---|---|---|
| 31 | 11 | |
| 2,525 | 11,544 | |
| 0.9% | 3.1% | |
| 8.4 | 9.5 | |
| about 1 month ago | 15 days ago | |
| Python | Jupyter Notebook | |
| Apache License 2.0 | MIT License |
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xTuring
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I'm developing an open-source AI tool called xTuring, enabling anyone to construct a Language Model with just 5 lines of code. I'd love to hear your thoughts!
Explore the project on GitHub here.
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LLaMA 2 fine-tuning made easier and faster
If you're curious, I encourage you to: - Dive deeper with the LLaMA 2 tutorial here. - Explore the project on GitHub here. - Connect with our community on Discord here.
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RAG vs. Fine-Tuning
If you want best performance, you need to do both RAG and fine-tuning very well. There are plenty of resources on doing fine-tuning thought. I'm one of the contributors to https://github.com/stochasticai/xturing project focused on fine-tuning LLMs. You can find help in the discord channel listed on the GitHub.
- Build, customize and control your own personal LLMs via xTuring OSS
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Finetuning LLaMA 2 (the base models) ?
What tools do you use and achieved great results ? … For me i have tried xturing and SFTTrainer and they got me a semi okay results.
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Finetuning using Google Colab (Free Tier)
Code: https://github.com/stochasticai/xTuring/blob/main/examples/llama/llama_lora_int8.py Colab: https://colab.research.google.com/drive/1SQUXq1AMZPSLD4mk3A3swUIc6Y2dclme?usp=sharing
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I would like to try my hand at finetuning some models. What is the best way to start? I have some questions that I'd appreciate your help on.
We are a group of researchers out of Harvard working on open-source library called xTuring, focused on fine-tuning LLMs: https://github.com/stochasticai/xturing.
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Fine tuning on my tweets
Fine tuning I was thinking about using this (low GPU memory footprint): https://github.com/stochasticai/xturing/blob/main/examples/int4_finetuning/README.md
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Colab for finetuning llama models in 4-bit?
I can't speak for QLORA, as I haven't had a chance to get an implementation working, but I've had success with StochasticAI's Xturing. It's by far the most streamlined method of finetuning I've come across, and they offer int8 and int4 fintuning (but only for llama-7B).
- Just wanna say this.
FinGPT
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GPT-4, without specialized training, beat a GPT-3.5 class model that cost $10B
There is also the open source FinGPT, that is claimed to beat GPT4 in some benchmarks at a fine tuning cost of $17.25.
https://github.com/AI4Finance-Foundation/FinGPT
- FLaNK Stack 05 Feb 2024
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Ask HN: How to Get into Quantitative Trading?
https://github.com/AI4Finance-Foundation/FinGPT
[1] Zipline is a Pythonic algorithmic trading library
- FinGPT
- FLaNK Stack Weekly for 20 June 2023
- GPT but for the Finance Industry
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FinGPT: Open-Source Financial Large Language Models
Code: https://github.com/AI4Finance-Foundation/FinGPT
- Is FinGPT Coming?
What are some alternatives?
quivr - Your GenAI Second Brain 🧠 A personal productivity assistant (RAG) ⚡️🤖 Chat with your docs (PDF, CSV, ...) & apps using Langchain, GPT 3.5 / 4 turbo, Private, Anthropic, VertexAI, Ollama, LLMs, Groq that you can share with users ! Local & Private alternative to OpenAI GPTs & ChatGPT powered by retrieval-augmented generation.
modelscope - ModelScope: bring the notion of Model-as-a-Service to life.
axolotl - Go ahead and axolotl questions
EasyCV - An all-in-one toolkit for computer vision
awesome-totally-open-chatgpt - A list of totally open alternatives to ChatGPT
keep - The open-source alert management and AIOps platform
Meshtasticator - Discrete-event and interactive simulator for Meshtastic.
prompt-engineering - Tips and tricks for working with Large Language Models like OpenAI's GPT-4.
Zicklein - Finetuning instruct-LLaMA on german datasets.
StanfordQuadruped
safetensors_util - Utility for Safetensors Files
alpaca_eval - An automatic evaluator for instruction-following language models. Human-validated, high-quality, cheap, and fast.