Update README.md

README.md

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-## Usage and limitations
-
-### Intended usage
-
-Open Vision Language Models (VLMs) have a wide range of applications across
-various industries and domains. The following list of potential uses is not
-comprehensive. The purpose of this list is to provide contextual information
-about the possible use-cases that the model creators considered as part of model
-training and development.
-
-* The model can be further fine-tuned on bigger and better dataset or your own custom dataset
-* The model can be used in apps to provide real-time visual and text-based assistance in Hindi and English.
-* The model can be a tool for researchers to develop new vision-language technologies and applications.
-
-### Ethical considerations and risks
-
-The development of vision-language models (VLMs) raises several ethical concerns. In creating an open model, we have carefully considered the following:
-
-*   Bias and Fairness
-    *   VLMs trained on large-scale, real-world image-text data can reflect socio-cultural biases embedded in the training material. These models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported in this card.
-*   Misinformation and Misuse
-    *   VLMs can be misused to generate text that is false, misleading, or harmful.
-    *   Guidelines are provided for responsible use with the model, see the [Responsible Generative AI Toolkit](https://ai.google.dev/responsible).
-*   Transparency and Accountability
-    *   This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes.
-    *   A responsibly developed open model offers the opportunity to share innovation by making VLM technology accessible to developers and researchers across the AI ecosystem.
-
-
-Risks identified and mitigations:
-
-*   **Perpetuation of biases:** It's encouraged to perform continuous monitoring
-    (using evaluation metrics, human review) and the exploration of de-biasing
-    techniques during model training, fine-tuning, and other use cases.
-*   **Generation of harmful content:** Mechanisms and guidelines for content
-    safety are essential. Developers are encouraged to exercise caution and
-    implement appropriate content safety safeguards based on their specific
-    product policies and application use cases.
-*   **Misuse for malicious purposes:** Technical limitations and developer and
-    end-user education can help mitigate against malicious applications of LLMs.
-    Educational resources and reporting mechanisms for users to flag misuse are
-    provided. Prohibited uses of Gemma models are outlined in the [Gemma
-    Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy).
-*   **Privacy violations:** Models were trained on data filtered to remove certain personal information and sensitive data. Developers are encouraged to adhere to privacy regulations with privacy-preserving techniques.
-
-### Limitations
-
-*   Most limitations inherited from the underlying Gemma model still apply:
-    *   VLMs are better at tasks that can be framed with clear prompts and
-        instructions. Open-ended or highly complex tasks might be challenging.
-    *   Natural language is inherently complex. VLMs might struggle to grasp
-        subtle nuances, sarcasm, or figurative language.
-    *   VLMs generate responses based on information they learned from their
-        training datasets, but they are not knowledge bases. They may generate
-        incorrect or outdated factual statements.
-    *   VLMs rely on statistical patterns in language and images. They might
-        lack the ability to apply common sense reasoning in certain situations.