awni

Setup the repo

git clone [email protected]:filipstrand/mflux.git
cd mflux && pip install -r requirements.txt

Make a run script

Name this anything, maybe flux.py. Make sure to update the two paths marked below.

awni

"""
A minimal, fast example generating text with Llama 3.1 in MLX.

To run, install the requirements:

    pip install -U mlx transformers fire

Then generate text with:

    python l3min.py "How tall is K2?"

sugatoray

awni

MLX LM with the OpenAI Python Package

1. Install

Install MLX LM and openai:

pip install mlx-lm openai

sugatoray

from great_tables import GT, md, html, system_fonts
import pandas as pd

power_cie_prepared_tbl = pd.read_csv("./data/2023_cie_power_cons.csv")

# Create a Great Tables object
ciep_gt_tbl = GT(data=power_cie_prepared_tbl)

# Apply wider color ranges & formatting
gt_tbl = ciep_gt_tbl \

chalg

from great_tables import GT, md, html, system_fonts
import pandas as pd

power_cie_prepared_tbl = pd.read_csv("./data/2023_cie_power_cons.csv")

# Create a Great Tables object
ciep_gt_tbl = GT(data=power_cie_prepared_tbl)

# Apply wider color ranges & formatting
gt_tbl = ciep_gt_tbl \

pdtgct

The LLaMA model weights may be converted from Huggingface PyTorch format back to GGML in two steps:

1. download from decapoda-research/llama-7b-hf and save as pytorch .pth
2. use the ggerganov/llama.cpp script, convert-pth-to-ggml.py to convert from pytorch .pth to GGML

This process will result in ggml model with float16 (fp16) precision.

Prerequisite

rasbt

# Sebastian Raschka 09/24/2022
# Create a new conda environment and packages
#   conda create -n whisper python=3.9
#   conda activate whisper
#   conda install mlxtend -c conda-forge

# Install ffmpeg
# macOS & homebrew
#   brew install ffmpeg
# Ubuntu

sugatoray

# https://gist.github.com/althonos/6914b896789d3f2078d1e6237642c35c

[metadata]
name = {name}
version = file: {name}/_version.txt
author = Martin Larralde
author_email = [email protected]
url = https://github.com/althonos/{name}
description = {description}
long_description = file: README.md

marcosci

library(LaCroixColoR)
library(sf)
library(fasterize)
library(rayshader)
library(raster)
library(exactextractr)
library(rayrender)

# load raster into R
elevation <- raster("Olympus_Mons_ortho-image.tif")