Fabrizio Milo Mistobaan

uv tool run opik harbor trials start -p ./examples/tasks/k8s-hello-world/ \
        --agent "terminus-2" \
        --agent-kwarg "api_base=$OPENAI_API_BASE" \
  --model "hosted_vllm/$MODEL" \
  --environment-type "docker"

OPIK: Opik tracking enabled for Harbor
Starting trial: k8s-hello-world__wv9bPao
Task: k8s-hello-world

Overview

A descriptio of all the steps to be able to debug Zed using Zed. This is useful if you want to add features and fix errors within the Zed codebase using Zed itself.

1. Compile Debugger Branch

First you need to download and compile the debugger branch:

	# # Serverless OpenThinker Agent v1 with vLLM and Modal

	# This example shows how to serve Hugging Face's
	# [open-thoughts/OpenThinker-Agent-v1](https://huggingface.co/open-thoughts/OpenThinker-Agent-v1)
	# with vLLM on Modal.
	# OpenThinker-Agent-v1 is an agentic model post-trained from Qwen/Qwen3-8B via supervised fine-tuning and reinforcement learning,
	# and it excels on agent benchmarks such as Terminal-Bench 2.0 and SWE-Bench.

	# We include the same Modal best practices as our other inference demos:
	# - GPU snapshots for faster cold starts

	#!/bin/bash

	# Check if the argument is provided
	if [ -z "$1" ]; then
	echo "Usage: $0 <path_storage>"
	exit 1
	fi


	PATH_STORAGE=$1

	<!DOCTYPE html>
	<html xmlns="http://www.w3.org/1999/xhtml" lang xml:lang>

	<head>
	<meta charset="utf-8" />
	<meta name="generator" content="nbh" />
	<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes" />
	<title>Notebook HTML Renderer</title>
	<!-- KaTeX -->
	<script src="https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.9/katex.min.js"

	# install node
	# Download and install nvm:
	curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.3/install.sh \| bash

	# in lieu of restarting the shell
	\. "$HOME/.nvm/nvm.sh"

	# Download and install Node.js:
	nvm install 22

	import math

	def compute_cost(num_nodes, gpus_per_node, cost_per_gpu_hour, days):
	"""
	Computes the cost of running a certain setup (num_nodes, gpus_per_node)
	for a specified number of days at a particular cost_per_gpu_hour.

	:param num_nodes: Number of nodes
	:param gpus_per_node: Number of GPUs in each node
	:param cost_per_gpu_hour: Cost per GPU-hour in USD

	from tqdm.auto import tqdm
	import asyncio
	import aiohttp
	import json
	import time
	import os
	import textwrap

	TOGETHER_API_TOKEN = os.environ['TOGETHER_API_TOKEN']

	# Example usage:
	# python merge_peft.py --base_model=meta-llama/Llama-2-7b-hf --peft_model=./qlora-out --hub_id=alpaca-qlora

	from transformers import AutoModelForCausalLM, AutoTokenizer
	from peft import PeftModel
	import torch

	import argparse

	def get_args():