OpenHathi-7B-Hi-v0.1-Base
Maintainer: sarvamai
89
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
OpenHathi-7B-Hi-v0.1-Base is a large language model developed by Sarvam AI that is based on Llama2 and trained on Hindi, English, and Hinglish data. It is a 7 billion parameter model, making it a mid-sized model compared to similar offerings like the alpaca-30b and PMC_LLAMA_7B models. This base model is designed to be fine-tuned on specific tasks, rather than used directly.
Model inputs and outputs
OpenHathi-7B-Hi-v0.1-Base is a text-to-text model, meaning it takes in text and generates new text in response. The model can handle a variety of language inputs, including Hindi, English, and code.
Inputs
- Text prompts in Hindi, English, or Hinglish
Outputs
- Generated text in response to the input prompt
Capabilities
OpenHathi-7B-Hi-v0.1-Base has broad capabilities in language generation, from open-ended conversation to task-oriented outputs. The model can be used for tasks like text summarization, question answering, and creative writing. It also has the potential to be fine-tuned for more specialized use cases, such as code generation or domain-specific language modeling.
What can I use it for?
The OpenHathi-7B-Hi-v0.1-Base model could be useful for a variety of applications that require language understanding and generation in Hindi, English, or a mix of the two. Some potential use cases include:
- Building virtual assistants or chatbots that can communicate in Hindi and English
- Generating content like news articles, product descriptions, or creative writing in multiple languages
- Translating between Hindi and English
- Providing language support for applications targeting Indian users
Things to try
One interesting thing to try with OpenHathi-7B-Hi-v0.1-Base would be to fine-tune it on a specific domain or task, such as customer service, technical writing, or programming. This could help the model learn the nuances and specialized vocabulary of that area, allowing it to generate more relevant and useful text. Additionally, exploring the model's performance on code-switching between Hindi and English could yield insights into its language understanding capabilities.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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