sarvamai
/

sarvam-1

@@ -3,197 +3,77 @@ library_name: transformers
 tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
 ## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 tags: []
 ---
+# Sarvam-2B
+Sarvam-2B 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-2b).
+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("Sarvam/sarvam-2b")
+tokenizer = AutoTokenizer.from_pretrained("Sarvam/sarvam-2b")
+# 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