AIAnytime

from dotenv import load_dotenv
from openai import OpenAI
from traceloop.sdk import Traceloop
from traceloop.sdk.decorators import workflow, task
import os

load_dotenv()

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
TRACeloop_API_KEY = os.getenv("TRACeloop_API_KEY")

AIAnytime

Running the CLI

npx https://github.com/google-gemini/gemini-cli

2. Or for a global installation:

npm install -g @google/gemini-cli

Gemini 2.5 Pro is available at 60 requests per minute and up to 1,000 requests per day .

AIAnytime

import re
from langchain.docstore.document import Document
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Qdrant
from qdrant_client import QdrantClient

# --- Step 1: Extract text from the document ---
def extract_text(file_path: str) -> str:
    """

AIAnytime

FROM ./Llama-3-ELYZA-JP-8B-q4_k_m.gguf
TEMPLATE """{{ if .System }}<|start_header_id|>system<|end_header_id|>

{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>

{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>

{{ .Response }}<|eot_id|>"""
PARAMETER stop "<|start_header_id|>"
PARAMETER stop "<|end_header_id|>"

AIAnytime

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from openai import OpenAI
from dotenv import load_dotenv
import os

load_dotenv()

app = FastAPI()
client = OpenAI()

AIAnytime

import requests

# Base endpoint
base_url = "https://r.jina.ai/"

# Input URL to be appended
input_url = "https://www.stateof.ai/"

# Full URL with the input URL appended after a plus (+) sign
full_url = base_url + input_url

AIAnytime

import streamlit as st

# App title
st.title('EcoOptimizer Carbon Footprint Calculator')

# Introduction
st.write('''
This tool calculates the Software Carbon Intensity (SCI) for EcoOptimizer, 
an AI-powered energy management system for commercial buildings by EcoTech Solutions.
''')

AIAnytime

training_arguments = TrainingArguments(
        output_dir="./results",
        per_device_train_batch_size=4,
        per_device_eval_batch_size=4,
        gradient_accumulation_steps=2,
        optim="adamw_8bit",
        logging_steps=50,
        learning_rate=1e-4,
        evaluation_strategy="steps",
        do_eval=True,

AIAnytime

## With Evaluation Harness
!pip install git+https://github.com/EleutherAI/lm-evaluation-harness.git
!pip install bitsandbytes
!pip install --upgrade transformers
!pip install auto-gptq optimum autoawq

!lm_eval --model hf --model_args pretrained=google/gemma-7b --tasks winogrande,hellaswag,arc_challenge --device cuda:0 --num_fewshot 1 --batch_size 8 --output_path ./eval_harness/gemma-7b
  
!lm_eval --model hf --model_args pretrained=google/gemma-7b --tasks winogrande,hellaswag,arc_challenge --device cuda:0 --num_fewshot 5 --batch_size 8 --output_path ./eval_harness/gemma-7b-5shot
  

AIAnytime

import os
#set you runpod key as a environment variable
os.environ['RUNPOD_API_KEY'] = "your_runpod_api_key"

import runpod
from IPython.display import display, Markdown

runpod.api_key = os.getenv("RUNPOD_API_KEY", "your_runpod_api_key")

if runpod.api_key == "your_runpod_api_key":