dhruvilp

# train.py
# Run with: accelerate launch --num_processes 4 train.py
# Make sure to have accelerate config set up for DDP, or it will auto.

import os
import torch
from datasets import load_dataset
from peft import LoraConfig, get_peft_model, TaskType
from transformers import (
    AutoModelForCausalLM,

dhruvilp

Granite Docling Document Converter

A high-performance, parallel-processing library for converting documents to Markdown, JSON, and DocTags using the Granite Docling model. No FastAPI, Flask, or web frameworks required - pure Python library with sync and async support.

🚀 Features

• No Web Framework Required: Pure Python library - use it directly in your code
• Parallel Processing: Process large PDFs with multiple workers for maximum speed
• Async Support: Full async/await support for non-blocking operations
• Multiple Output Formats: Convert to Markdown, JSON, DocTags

dhruvilp

[
  {
    "content": "reasoning language: English\n\nYou are an intelligent assistant that can answer customer service queries",
    "role": "system",
    "thinking": null
  },
  {
    "content": "Can you provide me with a list of the top-rated series currently on Netflix?",
 "role": "user",

dhruvilp

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import time


model_path = './gpt-oss-model-local'

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,     

dhruvilp

import json
import pandas as pd
from langchain.text_splitter import RecursiveCharacterTextSplitter
from openai import OpenAI
import os
from typing import List, Dict
import random

class SyntheticDataGenerator:
    def __init__(self, api_key: str = None, model: str = "gpt-4"):

dhruvilp

import asyncio
import json
import os
from base64 import b64decode
from typing import List, Dict, Optional, Any
from pydantic import BaseModel, Field

from crawl4ai import (
    AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode,
    JsonCssExtractionStrategy, LLMExtractionStrategy, LLMConfig,

dhruvilp

nference Providers
NEW

Fireworks
Text Generation
Reset
Examples
Input a message to start chatting with deepseek-ai/DeepSeek-V3-0324.
How can I convert an app running on tomcat Catalina 8 server to spring boot app with jdk 17

dhruvilp

from together import Together
client = Together(api_key = TOGETHER_API_KEY)

question = "Which is larger 9.9 or 9.11?"

thought = client.chat.completions.create(
  model="deepseek-ai/DeepSeek-R1",
  messages=[{"role": "user", "content": question}],
  stop = ['</think>']
)

dhruvilp

# pip install openai
import os
from openai import OpenAI

aiml_api_key ='<YOUR_AIML_API_KEY>'

client = OpenAI(
    api_key=aiml_api_key,
    base_url="https://api.aimlapi.com",
)

dhruvilp

"age";"job";"marital";"education";"default";"housing";"loan";"contact";"month";"day_of_week";"duration";"campaign";"pdays";"previous";"poutcome";"emp.var.rate";"cons.price.idx";"cons.conf.idx";"euribor3m";"nr.employed";"y"
56;"housemaid";"married";"basic.4y";"no";"no";"no";"telephone";"may";"mon";261;1;999;0;"nonexistent";1.1;93.994;-36.4;4.857;5191;"no"
57;"services";"married";"high.school";"unknown";"no";"no";"telephone";"may";"mon";149;1;999;0;"nonexistent";1.1;93.994;-36.4;4.857;5191;"no"
37;"services";"married";"high.school";"no";"yes";"no";"telephone";"may";"mon";226;1;999;0;"nonexistent";1.1;93.994;-36.4;4.857;5191;"no"
40;"admin.";"married";"basic.6y";"no";"no";"no";"telephone";"may";"mon";151;1;999;0;"nonexistent";1.1;93.994;-36.4;4.857;5191;"no"
56;"services";"married";"high.school";"no";"no";"yes";"telephone";"may";"mon";307;1;999;0;"nonexistent";1.1;93.994;-36.4;4.857;5191;"no"
45;"services";"married";"basic.9y";"unknown";"no";"no";"telephone";"may";"mon";198;1;999;0;"nonexistent";1.1;93.994;-36.4