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FrontiersMind/Nandi-Mini-150M-Instruct

🤗 Hugging Face sourcetext-generationapache-2.0153M params307 MBsafetensors✓ 2 checksumsupdated today
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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 = "FrontiersMind/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)

📬 Feedback & Suggestions

We’d love to hear your thoughts, feedback, and ideas!