tcapelle

import torch
from transformers import AutoTokenizer
from trl import GRPOConfig, GRPOTrainer
import re
import wandb
import accelerate
from math_verify import parse, verify, ExprExtractionConfig
from datasets import load_dataset, Dataset

accelerator = accelerate.Accelerator()

tcapelle

import modal
from modal import Image

from evals import EVALUATIONS_CONFIGS
from evals.runner import run_eval

GPU_TYPE = "L4"

# Create Modal app
app = modal.App(name="eval_scorers")

tcapelle

from dataclasses import dataclass
import simple_parsing as sp
from typing import Literal

import modal
from modal import Image

from evals import EVALUATIONS_CONFIGS
from evals.runner import run_eval

tcapelle

# put your wandb api key in the modal secrets
# >modal run eval_latency_modal.py

import time
import logging
import numpy as np
from rich.console import Console
from rich.table import Table
import modal
from modal import Image

tcapelle

# pip install weave openai atproto rich

import os
import openai
import weave
from atproto import Client
from rich.console import Console
from rich.rule import Rule
from datetime import datetime, timedelta, timezone
import warnings

tcapelle

import os, openai, weave

MODEL = "meta-llama/Llama-3.2-90B-Vision-Instruct"

weave.init("llama32_90B_EU")

image_url = "https://www.hachette.fr/sites/default/files/webmasters/l023-1.png"
llama_client = openai.OpenAI(
    base_url="http://195.242.25.198:8032/v1",
    api_key=os.environ.get("WANDB_API_KEY")

tcapelle

import os, openai, weave

MODEL = "Llama-3.2-90B-Vision-Instruct"

weave.init("EU_HAS_LLAMA_90B")

image_url = "https://limaspanishhouse.com/wp-content/uploads/2021/02/peruvian-llama-2-1536x1346.jpg"
llama_client = openai.OpenAI(
    base_url="http://195.242.25.198:8032/v1",
    api_key=os.environ.get("WANDB_API_KEY")

tcapelle

import asyncio
import os
import base64
from pathlib import Path
from mistralai import Mistral

image_path = "test_24915.jpg"
temperature = 0.0
max_tokens = None

tcapelle

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"

tcapelle

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