OpenHathi-7B-Hi-v0.1-Base
| Property | Value |
|---|---|
| Parameter Count | 6.87B |
| Model Type | Text Generation |
| Architecture | Llama2-based |
| License | Llama2 |
| Tensor Type | BF16 |
What is OpenHathi-7B-Hi-v0.1-Base?
OpenHathi-7B-Hi-v0.1-Base is the inaugural model in the OpenHathi series developed by Sarvam AI. It's a sophisticated 7B parameter language model built on the Llama2 architecture, specifically designed to handle Hindi, English, and Hinglish content. This base model represents a significant step forward in multilingual AI capabilities, particularly for Indian language processing.
Implementation Details
The model is implemented using the Transformers architecture and is available in both Safetensors and GGUF formats. It utilizes BF16 tensor type for efficient computation and memory usage. The model can be easily integrated using the Hugging Face Transformers library, with support for text-generation-inference and dedicated inference endpoints.
- Built on Llama2 architecture with 6.87B parameters
- Supports Hindi, English, and Hinglish text processing
- Implements BF16 precision for optimal performance
- Compatible with standard transformer-based workflows
Core Capabilities
- Multilingual text generation across Hindi, English, and Hinglish
- Base model functionality for custom fine-tuning
- Efficient processing with optimized tensor operations
- Seamless integration with popular ML frameworks
Frequently Asked Questions
Q: What makes this model unique?
OpenHathi-7B-Hi-v0.1-Base stands out for its specialized focus on Indian languages, particularly its ability to handle Hindi and Hinglish alongside English. It's built on the proven Llama2 architecture while being specifically optimized for Indian language processing needs.
Q: What are the recommended use cases?
As a base model, it's not intended for direct deployment. Users should fine-tune it for specific tasks such as content generation, translation, or language understanding applications. The model's multilingual capabilities make it particularly suitable for applications requiring Hindi and Hinglish language processing.
