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from mistralai import Mistral

import os
import weave

weave.init("pixtral")

print("1. La Platforme Mistral")
api_key = os.environ["MISTRAL_API_KEY"]
model = "pixtral-12b-2409"

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from dataclasses import dataclass
from pathlib import Path
import simple_parsing


# clone weave:
# git clone https://github.com/wandb/weave
WEAVE_DOCS_PATH = Path("weave/docs/docs")

@dataclass

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name: Build and Test Locally
on: 
  workflow_dispatch: #allows repo admins to trigger this workflow from the Actions tab
    inputs:
      create_github_issue:
        type: boolean
        description: 'Create issues on failing notebooks'     
        default: false
      nbs_list:
        type: choice

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Linux

Setup

When setting up a fresh VM.

sudo apt update
sudo apt-get install vim tmux wget

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My Personal Top Wines: by Region

This is an arbitrary good list of wines I like, don't be offended if your favorite wine is not there. I have limited tasting time, and have mostly choose little family owned producers

Everything here is Organic, most are biodynamic and some are natural.

I have visited most of the winerys myself, and now I order directly from them or by my local wine-shop that actually helps me find more wine of this quality.

Loire

Probably my fav white wine region, I love the floral and sometimes minerality of these wines. There is always a good reason to open any of these bottles. It is also a very beautiful region to visit where you find accomodation on old castles like Chteau de Beaujeu.

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import wandb
from wandb import Api

api = Api()
ENTITY = "fastai"
PROJECT = "fine_tune_timm"

project = api.project(PROJECT, entity=ENTITY)

sweeps = project.sweeps()

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import os
import openai
from rich.console import Console

console = Console()

openai.api_key = os.getenv("OPENAI_API_KEY")
    
history = [{"role": "system", "content": "You are a helpful assistant."},]

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import wandb
import timm
import argparse
from fastai.vision.all import *
from fastai.callback.wandb import WandbCallback
from torchvision import models

def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument('--batch_size', type=int, default=64)

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