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license: apache-2.0
language:
- en
- hi
- mr
- ta
- te
- kn
- ml
- bn
- pa
- gu
- or
pipeline_tag: text-generation
library_name: transformers
base_model:
- FrontiersMind/Nandi-Mini-150M
---
# 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
```python
!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!
- **Discord**: https://discord.gg/ZGdjCdRt
- **Email:** support@frontiersmind.ai
- **Official Website** https://www.frontiersmind.ai/
- **LinkedIn:** https://www.linkedin.com/company/frontiersmind/
- **X (Twitter):** https://x.com/FrontiersMind |