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metadata
library_name: transformers
language:
  - bn
  - en
  - gu
  - hi
  - kn
  - ml
  - mr
  - or
  - pa
  - ta
  - te

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: 38.22
  • ARC-Challenge: 46.71
  • TriviaQA: 86.11
  • BoolQ: 62.59

IndicGenBench (One-shot)

  • Flores English-to-Indic translation: 46.81 chrF++
  • CrossSum: 20.88 chrF++
  • XORQA: 26.47 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