lewtun

<|im_start|>system
You are a strategic multi-tool AI agent planner for Round 1. Plan parallel execution of independent tools for this round.<|im_end|>
<|im_start|>user
You are a strategic decision-making expert for a multi-tool AI agent using the provided tools to perform the task.
        
        TASK: "Hey, I’m working on this new dashboard that pulls search results from three different services—one for AI stuff, one for code hosting, and one for edge networking—and I’m scratching my head over how each handles pagination. Some APIs might use a page/page_size setup, others a cursor or next_cursor, and I’m not even sure if all of them support paging in their search calls or if I have to switch to their “list” routes instead. 

Could you dig into each service’s search endpoints and tell me:
• whether it pages at all or not  
• if it does, what style it uses (page numbers, cursors, etc.)  

lewtun

# pip install emoji
import argparse
from datasets import load_dataset
import emoji

def remove_emoji(text: str) -> str:
    return emoji.replace_emoji(text, replace='').strip()

def format_messages(x):
    emojis_found = False

lewtun

{# ───── defaults ───── #}
{%- if enable_thinking is not defined -%}
{%- set enable_thinking = true -%}
{%- endif -%}

{# ───── reasoning mode ───── #}
{%- if enable_thinking -%}
  {%- set reasoning_mode = "/think" -%}
{%- else -%}
  {%- set reasoning_mode = "/no_think" -%}

lewtun

from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig, set_seed
import time
import torch

set_seed(0)
device = "cuda"
model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-1.5B",
    attn_implementation="flash_attention_2",
    torch_dtype="bfloat16"

lewtun

from datasets import load_dataset
from trl import GRPOConfig, GRPOTrainer
import random
"""Usage (on 8 x H100s):

pip install vllm==0.7.0 --extra-index-url https://download.pytorch.org/whl/cu121
pip install -e '.[dev]'

# DDP
accelerate launch --config_file examples/accelerate_configs/multi_gpu.yaml --num_processes 7 scratch/grpo_demo.py

lewtun

from typing import Optional

def extract_boxed_solution(text: str) -> Optional[str]:
    """
    Extracts the content of the last `\boxed{}` in a given LaTeX-style text.

    Args:
        text (str): The input string containing LaTeX-style content.

    Returns:

lewtun

# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

lewtun

# flake8: noqa
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software

lewtun

{
  "config_general": {
    "lighteval_sha": "?",
    "num_fewshot_seeds": 1,
    "override_batch_size": 4,
    "max_samples": null,
    "job_id": "",
    "start_time": 1163608.425196265,
    "end_time": 1173616.769654949,
    "total_evaluation_time_secondes": "10008.34445868386",

lewtun

"""
First install: pip install datasets pandas rich transformers

Usage:

# Loglikelihood evals
python view_details.py --filepath path/to/parquet/details 

# Generative evals
python view_details.py --filepath path/to/parquet/details --is_generative