LLM-As-Chatbot
simple-llm-finetuner
| LLM-As-Chatbot | simple-llm-finetuner | |
|---|---|---|
| 3 | 12 | |
| 3,241 | 1,977 | |
| - | - | |
| 9.0 | 10.0 | |
| 6 months ago | 5 months ago | |
| Python | Jupyter Notebook | |
| Apache License 2.0 | MIT License |
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LLM-As-Chatbot
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OpenAI's GPT-4 Red Teamer Nathan Labenz: the GPT-4 base model recommends assassinating humans, naming specific targets
The first one is from https://github.com/deep-diver/Alpaca-LoRA-Serve
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Show HN: ChatLLaMA – A ChatGPT style chatbot for Facebook's LLaMA
this is useless because it doesn't handle context:
Q: Name five genres of music.
A: Jazz, country, hip-hop, blues, classical.
Q: Name a famous artist from the third genre.
A: Salvador Dalí.
Whereas this one actually supports context: https://github.com/deep-diver/Alpaca-LoRA-Serve
- Show HN: Finetune LLaMA-7B on commodity GPUs using your own text
simple-llm-finetuner
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Ask HN: Resource to learn how to train and use ML Models
Just the appropriate reddit groups and follow folks on twitter, plus use a search engine.
1. Learn to run a model, checkout llama.cpp Tons of free models on huggingface.com
2. Learn to finetune a model - https://github.com/lxe/simple-llm-finetuner
3. Learn to train one. PyTorch, TensorFlow, HuggingFace libraries, etc.
Good luck.
- How can I train my custom dataset on top of Vicuna?
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[D] The best way to train an LLM on company data
So as far as set up goes, you just need to: “”” Git clone https://github.com/lxe/simple-llama-finetuner Cd simple-llama-finetuner Pip install -r requirements.txt Python app.py ## if you’re on a remote machine (Paperspace is my go to) then you may need to edit the last line of this script to set ‘share=True’ in the launch args “””
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Show HN: Document Q&A with GPT: web, .pdf, .docx, etc.
oobabooga's textgen webui has a tab for fine tuning now. You only need a single consumer GPU to fine tune up to 33B parameter models at a rate of about 200 epochs per hour, per GPU.
There are also one-click finetuning projects which run on free Google Colab GPUs like https://github.com/lxe/simple-llama-finetuner
It's easy and not complex at all.
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How do I fine tune 4 bit or 8 bit models?
for a single 4090, easiest way to get started and simple to use: https://github.com/lxe/simple-llama-finetuner
- Are there publicly available datasets other than Alpaca that we can use to fine-tune LLaMA?
- Show HN: Finetune LLaMA-7B on commodity GPUs using your own text
- [Project] Finetune LLaMA-7B on commodity GPUs (and Colab) using your own text
What are some alternatives?
alpaca-lora - Instruct-tune LLaMA on consumer hardware
peft - 🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
paper-qa - LLM Chain for answering questions from documents with citations
hh-rlhf - Human preference data for "Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback"
alpaca-7b-truss
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
stanford_alpaca - Code and documentation to train Stanford's Alpaca models, and generate the data.
minimal-llama
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM (Android/Linux/Windows/Mac)
OpenChatKit