Update README.md

README.md

@@ -1,9 +1,11 @@
+---
 library_name: transformers
 tags:
   - AI
   - NLP
   - LLM
   - ML
+  - Generative AI
 language:
   - en
 metrics:
@@ -16,7 +18,7 @@ pipeline_tag: text2text-generation
 
 # Model Card for TinyLlama-1.1B Fine-tuned on NLP, ML, Generative AI, and Computer Vision Q&A
 
-This model is fine-tuned on the **TinyLlama-1.1B** base model to answer domain-specific questions in **Natural Language Processing (NLP), Machine Learning (ML), Deep Learning (DL), Generative AI, and Computer Vision (CV)**. It generates accurate and context-aware responses, making it suitable for educational, research, and professional applications.
+This model is fine-tuned from the **TinyLlama-1.1B** base model to provide answers to domain-specific questions in **Natural Language Processing (NLP)**, **Machine Learning (ML)**, **Deep Learning (DL)**, **Generative AI**, and **Computer Vision (CV)**. It generates accurate and context-aware responses, making it suitable for educational, research, and professional applications.
 
 ---
 
@@ -24,7 +26,7 @@ This model is fine-tuned on the **TinyLlama-1.1B** base model to answer domain-s
 
 ### Model Description
 
-This model is designed to excel in providing concise, domain-specific answers to questions in AI-related fields. By leveraging the powerful TinyLlama architecture and fine-tuning on a curated dataset of Q&A pairs, it ensures relevance and coherence in responses.
+This model excels in providing concise, domain-specific answers to questions in AI-related fields. Leveraging the powerful TinyLlama architecture and fine-tuning on a curated dataset of Q&A pairs, it ensures relevance and coherence in responses.
 
 - **Developed by:** Harikrishnan46624
 - **Funded by:** Self-funded
@@ -32,58 +34,61 @@ This model is designed to excel in providing concise, domain-specific answers to
 - **Model Type:** Text-to-Text Generation
 - **Language(s):** English
 - **License:** Apache 2.0
-- **Finetuned from:** TinyLlama-1.1B
+- **Fine-tuned from:** TinyLlama-1.1B
 
 ---
 
 ### Model Sources
 
-- **Repository:** [Fine-Tuning Notebook on GitHub](https://github.com/Harikrishnan46624/EduBotIQ/blob/main/Fine_tune/TinyLlama_fine_tuning.ipynb) 
-- **Demo:** [More Information Needed]
+- **Repository:** [Fine-Tuning Notebook on GitHub](https://github.com/Harikrishnan46624/EduBotIQ/blob/main/Fine_tune/TinyLlama_fine_tuning.ipynb)
+- **Demo:** [Demo Link to be Added]
 
 ---
 
-## Uses
+## Use Cases
 
 ### Direct Use
 
 - Answering technical questions in **AI**, **ML**, **DL**, **LLMs**, **Generative AI**, and **Computer Vision**.
-- Supporting educational content creation and research discussions.
+- Supporting educational content creation, research discussions, and technical documentation.
 
 ### Downstream Use
 
-- Fine-tuning for specific industries or applications, such as healthcare or finance.
-- Integrating into domain-specific chatbots or virtual assistants.
+- Fine-tuning for industry-specific applications like healthcare, finance, or legal tech.
+- Integrating into specialized chatbots, virtual assistants, or automated knowledge bases.
 
 ### Out-of-Scope Use
 
 - Generating non-English responses (English-only capability).
-- Handling tasks unrelated to the AI domain.
+- Handling non-technical, unrelated queries outside the AI domain.
 
 ---
 
 ## Bias, Risks, and Limitations
 
-- **Bias:** Trained on domain-specific datasets, the model may exhibit biases towards AI-related terminologies or fail to generalize well in other contexts.
-- **Risks:** May generate misleading or incorrect information if the query is ambiguous or beyond its scope.
-- **Limitations:** Struggles with highly complex, non-technical, or nuanced queries unrelated to its training data.
+- **Bias:** Trained on domain-specific datasets, the model may exhibit biases toward AI-related terminologies or fail to generalize well in other domains.
+- **Risks:** May generate incorrect or misleading information if the query is ambiguous or goes beyond the model’s scope.
+- **Limitations:** May struggle with highly complex or nuanced queries not covered in its fine-tuning data.
 
 ---
 
 ### Recommendations
 
-- Use the model in conjunction with human oversight for critical applications.
-- Regularly review and update fine-tuning datasets to ensure performance remains aligned with evolving domain knowledge.
+- For critical or high-stakes applications, it’s recommended to use the model with human oversight.
+- Regularly update the fine-tuning datasets to ensure alignment with the latest research and advancements in AI.
 
 ---
 
 ## How to Get Started
 
-To get started with the model, use the following code snippet:
+To use the model, install the `transformers` library and use the following code snippet:
 
 ```python
 from transformers import pipeline
 
+# Load the model
 model = pipeline("text2text-generation", model="TinyLlama/TinyLlama-1.1B-Chat-v1.0")
+
+# Generate a response
 output = model("What is the difference between supervised and unsupervised learning?")
 print(output)