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README.md

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+
+---
+
+library_name: transformers
+language:
+- bn
+- en
+- gu
+- hi
+- kn
+- ml
+- mr
+- or
+- pa
+- ta
+- te
+
+---
+
+[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
+
+
+# QuantFactory/sarvam-1-GGUF
+This is quantized version of [sarvamai/sarvam-1](https://huggingface.co/sarvamai/sarvam-1) created using llama.cpp
+
+# Original Model Card
+
+
+
+# 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.
+
+*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
+
+```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