|
--- |
|
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](https://www.sarvam.ai/blogs/sarvam-1). |
|
|
|
The model was trained with [NVIDIA NeMo™ Framework](https://github.com/NVIDIA/NeMo) on the Yotta Shakti Cloud using HGX H100 systems. |
|
|
|
## 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 |
|
|
|
### Academic Benchmarks (Zero-shot) |
|
|
|
- Flores English-to-Indic translation: 46.81 chrF++ |
|
- CrossSum: 20.88 chrF++ |
|
- XORQA: 26.47 F1 |
|
- XQUAD: 41.58 F1 |
|
|
|
### IndicGenBench (One-shot) |
|
|
|
- Flores English-to-Indic translation: 46.81% BLEU |
|
- CrossSum: 20.88% chrF++ |
|
- XORQA: 26.47% F1 |
|
- XQUAD: 41.58% F1 |
|
|
|
## Usage |
|
|
|
```python |
|
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](LICENSE.md) 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 |
