Nandi-Mini-150M-Instruct

Introduction

Nandi-Mini-150M-Instruct is a compact, efficient multilingual language model designed for strong performance in resource-constrained environments. It is pre-trained from scratch on 525 billion tokens and further enhanced through instruction tuning and Direct Preference Optimization (DPO). The model supports English and 10 Indic languages.

Nandi-Mini-150M-Instruct focuses on maximizing performance per parameter through architectural efficiency rather than scale. It is optimized for edge devices, on-prem deployments, and low-latency applications, making it ideal for resource-constrained environments. Nandi-Mini-150M-Instruct brings the following key features:

Strong multilingual capability across English and Indic languages
Efficient design enabling high performance at small scale (150M parameters)
Reduced memory footprint using factorized embeddings
Better parameter efficiency through layer sharing

📝 Upcoming Releases & Roadmap

We’re just getting started with the Nandi series 🚀

Nandi-Mini-150M-Tool-Calling (Specialized-Model) — Coming Soon this week
Nandi-Mini-500M (Base + Instruct) — Pre-Training Going On
Nandi-Mini-1B (Base + Instruct) — Pre-Training Going On

📢 Blogs & technical deep-dives coming soon, where we’ll share:

Architecture decisions and design trade-offs
Training insights and dataset composition
Benchmarks and real-world applications

Stay tuned!

🌍 Supported Languages

The model is trained on English and a diverse set of Indic languages, including:

Hindi, Bengali, Tamil, Telugu, Marathi, Gujarati, Kannada, Malayalam, Punjabi, Odia

🚀 Usage

!pip install transformers=='5.4.0'

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_name = "Rta-AILabs/Nandi-Mini-150M-Instruct"

device = "cuda" if torch.cuda.is_available() else "cpu"

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    trust_remote_code=True,
    dtype=torch.bfloat16
).to(device).eval()

prompt = "Explain newton's second law of motion"

messages = [
    {"role": "user", "content": prompt}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **inputs,
    max_new_tokens=500,
    do_sample=True,
    temperature=0.3,
    top_p=0.90,
    top_k=20,
    repetition_penalty=1.1,
)

generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

print(response)