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--model | |
/workspace/codesandbox-template-blank/llamafile/AutoCoder_S_6.gguf | |
--server | |
--host | |
0.0.0.0 | |
-ngl | |
100 | |
... |
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Converting HuggingFace Models to GGUF/GGML | |
Downloading a HuggingFace model | |
Running llama.cpp convert.py on the HuggingFace model | |
(Optionally) Uploading the model back to HuggingFace | |
Downloading a HuggingFace model | |
There are various ways to download models, but in my experience the huggingface_hub library has been the most reliable. The git clone method occasionally results in OOM errors for large models. | |
Install the huggingface_hub library: | |
pip install huggingface_hub |
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#https://tcude.net/creating-cloudflare-tunnels-on-ubuntu/ | |
#https://github.com/ollama/ollama/blob/main/docs/faq.md#how-can-i-use-ollama-with-ngrok | |
#------------------------------------ | |
wget -q https://github.com/cloudflare/cloudflared/releases/latest/download/cloudflared-linux-amd64.deb | |
dpkg -i cloudflared-linux-amd64.deb | |
cloudflared tunnel --url http://localhost:11434 --http-host-header="localhost:11434" |
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Running a MongoDB container with data export | |
This script demonstrates how to set up and use a MongoDB container to export data. | |
Pull the mongo image: | |
docker pull mongo | |
docker volume create mymongodata | |
Activate docker Container | |
docker container run -d --name some-mongo -e MONGO_INITDB_ROOT_USERNAME=mongoadmin -e MONGO_INITDB_ROOT_PASSWORD=mongopasswd -v mymongodata:/data/db -p 27017:27017 mongo |
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apt install mysql-server | |
service mysql start | |
mysql -e "ALTER USER 'root'@'localhost' IDENTIFIED WITH 'mysql_native_password' BY 'root';FLUSH PRIVILEGES;" | |
#username/password:root/root | |
mysql -u root -p |
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curl https://gist.githubusercontent.com/rajivmehtaflex/a1e6de2355e7a16bcd9f38f43a58794b/raw/0636ecab2e4f83c441dbc1b55b562850c3e66859/conda_install.sh | sh | |
conda config --set auto_activate_base false | |
conda create --name crewexp python=3.10 -y | |
conda activate crewexp | |
pip install poetry | |
cd gdata/ |
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curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh | |
source $HOME/.cargo/env | |
cargo --version | |
sudo apt install libssl-dev | |
sudo apt install pkg-config | |
mkdir ~/.cache/huggingface | |
touch ~/.cache/huggingface/token | |
echo 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 > ~/.cache/huggingface/token | |
git clone https://github.com/EricLBuehler/mistral.rs.git | |
cd mistral.rs |
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#——Install Package——— | |
pip install -U "huggingface_hub[cli]" | |
huggingface-cli login | |
HF_HUB_ENABLE_HF_TRANSFER=1 | |
#———Download Package——— | |
huggingface-cli download QuantFactory/Meta-Llama-3-120B-Instruct-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf' | |
#————Merge it———————— |
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import concurrent.futures | |
from typing import Any, Callable, List | |
from tqdm import tqdm | |
def concurrent_task_executor(task: Callable[[Any], None], data_list: List[Any], max_workers: int = 32, description: str = None) -> None: | |
""" | |
Execute tasks concurrently on a list of data objects using ThreadPoolExecutor. | |
Args: | |
task (Callable): The function to apply to each data object. |
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# How to install terraform | |
brew tap hashicorp/tap | |
brew install hashicorp/tap/terraform | |
terraform -help | |
terraform --version |