---
library_name: transformers
tags:
  - AI
  - NLP
  - LLM
  - ML
  - Generative AI
language:
  - en
metrics:
  - accuracy
  - bleu
base_model:
  - TinyLlama/TinyLlama-1.1B-Chat-v1.0
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 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.

---

## Model Details

### Model Description

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
- **Shared by:** Harikrishnan46624
- **Model Type:** Text-to-Text Generation
- **Language(s):** English
- **License:** Apache 2.0
- **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:** [Demo Link to be Added]

---

## Use Cases

### Direct Use

- Answering technical questions in **AI**, **ML**, **DL**, **LLMs**, **Generative AI**, and **Computer Vision**.
- Supporting educational content creation, research discussions, and technical documentation.

### Downstream Use

- 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 non-technical, unrelated queries outside the AI domain.

---

## Bias, Risks, and Limitations

- **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

- 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 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)