Prince-1/sarvam-1

language:

• bn
• en
• gu
• hi
• kn
• ml
• mr
• or
• pa
• ta
• te base_model:
• sarvamai/sarvam-1 tags:
• onnx
• onnxruntime-genai

Sarvam-1

Sarvam-1 is a 2-billion parameter language model specifically optimized for Indian languages. It provides best in-class performance in 10 Indic languages (bn, gu, hi, kn, ml, mr, or, pa, ta, te) when compared with popular models like Gemma-2-2B and Llama-3.2-3B. It is also competitive against the much larger models like Llama-3.1-8B in these languages. More details can be found in our release blog.

The model was trained with NVIDIA NeMo™ Framework on the Yotta Shakti Cloud using HGX H100 systems.

Note: This is a text-completion model. It is meant to be finetuned on downstream tasks, and cannot be used directly as a chat or an instruction-following model.

Key Features

• Optimized for 10 Indian Languages: Built from the ground up to support major Indian languages alongside English
• Superior Token Efficiency: Achieves fertility rates of 1.4-2.1 across all supported languages, 2-4x more efficient than existing multilingual models
• High-Quality Training Data: Trained on a curated corpus of ~4 trillion tokens with 2 trillion high-quality Indic tokens
• Efficient Inference: 4-6x faster inference compared to larger models while matching or exceeding their performance on Indic language tasks

Model Architecture

• Hidden size: 2048
• Intermediate size: 11,008
• Number of attention heads: 16
• Number of hidden layers: 28
• Number of key-value heads: 8
• Maximum position embeddings: 8,192
• Activation function: SwiGLU
• Positional embeddings: Rotary (RoPE) with theta=10,000
• Training: Grouped-query attention and bfloat16 mixed-precision

Performance

Translated Academic Benchmarks (Zero-shot)

• MMLU: 44.44
• ARC-Challenge: 58.50
• TriviaQA: 90.62
• BoolQ: 80.68

IndicGenBench (One-shot)

• Flores English-to-Indic translation: 39.83 chrF++
• CrossSum: 20.48 chrF++
• XORQA: 25.27 F1
• XQUAD: 41.58 F1

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("sarvamai/sarvam-1")
tokenizer = AutoTokenizer.from_pretrained("sarvamai/sarvam-1")

# Example usage
text = "कर्नाटक की राजधानी है:"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=5)
result = tokenizer.decode(outputs[0])

Training Details

• Training Infrastructure: Yotta's Shakti cluster
• Hardware: 1,024 GPUs
• Training Duration: 5 days
• Framework: NVIDIA NeMo

License

Sarvam non-commercial license: See the LICENSE file

Acknowledgements

• NVIDIA: for support with the NeMo codebase
• Yotta: for sccess to the Shakti GPU cluster
• AI4Bharat: for their academic partnership and expertise in Indian language technologies