knox / Flowise-5.sh
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| 1 | docker run -d --name flowise -p 3000:3000 flowise |
knox / Flowise-4.sh
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| 1 | docker build --no-cache -t flowise . |
knox / Flowise-3.sh
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| 1 | npx flowise start --FLOWISE_USERNAME=user --FLOWISE_PASSWORD=1234 |
knox / Flowise-2.sh
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| 1 | npx flowise start |
knox / Flowise-1.sh
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| 1 | npm install -g flowise |
knox / GPT4All-3.bib
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| 1 | @misc{gpt4all, |
| 2 | author = {Yuvanesh Anand and Zach Nussbaum and Brandon Duderstadt and Benjamin Schmidt and Andriy Mulyar}, |
| 3 | title = {GPT4All: Training an Assistant-style Chatbot with Large Scale Data Distillation from GPT-3.5-Turbo}, |
| 4 | year = {2023}, |
| 5 | publisher = {GitHub}, |
| 6 | journal = {GitHub repository}, |
| 7 | howpublished = {\url{https://github.com/nomic-ai/gpt4all}}, |
| 8 | } |
knox / GPT4All-2.py
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| 1 | from gpt4all import GPT4All |
| 2 | model = GPT4All("Meta-Llama-3-8B-Instruct.Q4_0.gguf") # downloads / loads a 4.66GB LLM |
| 3 | with model.chat_session(): |
| 4 | print(model.generate("How can I run LLMs efficiently on my laptop?", max_tokens=1024)) |
knox / GPT4All-1.sh
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| 1 | pip install gpt4all |
knox / Qdrant-readme-4.py
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| 1 | qdrant = QdrantClient("http://localhost:6333") # Connect to existing Qdrant instance |
knox / Qdrant-readme-3.sh
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| 1 | docker run -p 6333:6333 qdrant/qdrant |