A 4-bit quantized, business-focused conversational AI built on Mistral 7B using Unsloth. Optimized for low VRAM (runs on 6GB+ VRAM) and for Indian business use-cases. - 7B params in 4-bit (BF16 base): ~4GB RAM me chal jayega from unsloth import FastLanguageModel model, tokenizer = FastLanguageModel.frompretrained( modelname = "UX4567/Khushi-Business-AI-4bit", maxseqlength = 2048, loadin4bit = True, prompt = "Ek customer bol raha hai: Mujhe refund chahiye, kya reply du?" inputs = tokenizer([prompt], returntensors = "pt").to("cuda") outputs = model.generate(inputs, maxnewtokens = 200) print(tokenizer.batchdecode(outputs)[0])
Open weights
apache-2.0
7.2B parameters
32,768 tokens
India's Own Vedic Astrology AI - Ab aapka Kundli Expert aapke laptop pe! Ye model Vedic Astrology, Kundli, Rashi, Nakshatra, Dasha pe specially fine-tune kiya gaya hai. 3B size ka hai isliye aapke laptop / mobile pe bhi tez chalega. - "Meri Mesh rashi ka aaj ka bhavishya kya hai?" - "Kundli me Mangal Dosh kya hota hai?" - "Mere janm nakshatra ka matlab kya hai?" - "Shani ki sade sati kab khatam hogi?" Ye model Medical, Legal ya Financial advice ke liye nahi hai. Ye sirf educational astrology purpose ke liye hai. from transformers import AutoModelForCausalLM, AutoTokenizer modelid = "UX4567/Kartik-Kundli-AI-3B-v2.0" tokenizer = AutoTokenizer.frompretrained(modelid) model =…
Open weights
mit
3.1B parameters
32,768 tokens
transformers
