content-chatbot
simple-llm-finetuner
| content-chatbot | simple-llm-finetuner | |
|---|---|---|
| 5 | 12 | |
| 510 | 1,977 | |
| - | - | |
| 6.9 | 10.0 | |
| 3 months ago | 4 months ago | |
| Python | Jupyter Notebook | |
| - | MIT License |
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content-chatbot
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Show HN: Document Q&A with GPT: web, .pdf, .docx, etc.
I built this repo to do this for your own website content, that should get you a good starting point:
https://github.com/mpaepper/content-chatbot
- Your website's content -> Q&A bot / chatbot
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Repo to create embeddings of your website's content for a Q&A bot / chatbot
Thanks for sharing the code. What happen when the existing content get updated and new contents created, would it need to create embeddings for all contents again? The current approach is not good as create embeddings cost money? Please see https://github.com/mpaepper/content-chatbot/blob/main/create.... Would it be possible progressively update the vector store?
Please advise. Thank you.
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?
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
alpaca-lora - Instruct-tune LLaMA on consumer hardware
faiss - A library for efficient similarity search and clustering of dense vectors.
paper-qa - LLM Chain for answering questions from documents with citations
peft - 🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
slothbot - SlothBot | A generally useful analytical Discord bot that does support and writes SQL.
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
minimal-llama
OpenChatKit
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
WebChatRWKVstic - ChatGPT-like Web UI for RWKVstic