The tested environment is
$ uname -a
Linux richardfeynman 5.15.133.1-microsoft-standard-WSL2 #1 SMP Thu Oct 5 21:02:42 UTC 2023 x86_64 x86_64 x86_64 GNU/Linux$ cat /etc/lsb-release
DISTRIB_ID=Ubuntu
DISTRIB_RELEASE=22.04
DISTRIB_CODENAME=jammy
DISTRIB_DESCRIPTION="Ubuntu 22.04.3 LTS"$ poetry --version
Poetry (version 1.5.1)$ /usr/local/cuda/bin/nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2022 NVIDIA Corporation
Built on Wed_Sep_21_10:33:58_PDT_2022
Cuda compilation tools, release 11.8, V11.8.89
Build cuda_11.8.r11.8/compiler.31833905_0$ nvidia-smi --query-gpu=gpu_name,memory.total --format=csv
name, memory.total [MiB]
NVIDIA GeForce RTX 3060, 12288 MiBUsing poetry
poetry installor using the provided requirements.txt with pip
pip install -r requirements.txtEach of the notebooks are representable as slides in a browser, i.e. for notebooks/00_introduction.ipynb, run
jupyter nbconvert notebooks/00_introduction.ipynb --to slides --post serveThis will open a browser window with the notebook rendered as slides.
- https://learn.microsoft.com/en-us/azure/search/retrieval-augmented-generation-overview
- https://python.langchain.com/docs/expression_language/cookbook/retrieval
- original model: https://huggingface.co/mistralai/Mistral-7B-v0.1
- quantized model: https://huggingface.co/TheBloke/Mistral-7B-v0.1-AWQ
- vllm: https://docs.vllm.ai/
- autoawq:
- https://huggingface.co/TheBloke/SciPhi-Self-RAG-Mistral-7B-32k-AWQ
- https://github.com/oobabooga/text-generation-webui
- https://medium.com/@thakermadhav/build-your-own-rag-with-mistral-7b-and-langchain-97d0c92fa146
- https://redis.io/docs/get-started/vector-database/
- https://github.com/facebookresearch/faiss/wiki
- https://github.com/TimDettmers/bitsandbytes
- https://www.youtube.com/watch?v=IxrlHAJtqKE
- https://research.ibm.com/blog/retrieval-augmented-generation-RAG
- NLP Course: https://huggingface.co/learn/nlp-course/chapter1/1
- Quantization: https://huggingface.co/docs/transformers/main_classes/quantization
- Tokenizer: https://huggingface.co/docs/transformers/main_classes/tokenizer
- https://python.langchain.com/docs/integrations/vectorstores/faiss
- https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf
- https://openai.com/blog/introducing-text-and-code-embeddings
- RAG: https://arxiv.org/abs/2005.11401v4
- MistralAI paper: https://arxiv.org/pdf/2310.06825.pdf
- OpenAI Text embeddings paper: https://arxiv.org/abs/2201.10005
- Similarity search: https://arxiv.org/abs/1702.08734