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--- |
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library_name: transformers |
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tags: |
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- AI |
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- NLP |
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- LLM |
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- ML |
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- Generative AI |
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language: |
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- en |
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metrics: |
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- accuracy |
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- bleu |
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base_model: |
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- TinyLlama/TinyLlama-1.1B-Chat-v1.0 |
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pipeline_tag: text2text-generation |
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--- |
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# Model Card for TinyLlama-1.1B Fine-tuned on NLP, ML, Generative AI, and Computer Vision Q&A |
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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. |
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## Model Details |
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### Model Description |
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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. |
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- **Developed by:** Harikrishnan46624 |
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- **Funded by:** Self-funded |
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- **Shared by:** Harikrishnan46624 |
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- **Model Type:** Text-to-Text Generation |
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- **Language(s):** English |
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- **License:** Apache 2.0 |
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- **Fine-tuned from:** TinyLlama-1.1B |
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### Model Sources |
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- **Repository:** [Fine-Tuning Notebook on GitHub](https://github.com/Harikrishnan46624/EduBotIQ/blob/main/Fine_tune/TinyLlama_fine_tuning.ipynb) |
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- **Demo:** [Demo Link to be Added] |
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## Use Cases |
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### Direct Use |
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- Answering technical questions in **AI**, **ML**, **DL**, **LLMs**, **Generative AI**, and **Computer Vision**. |
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- Supporting educational content creation, research discussions, and technical documentation. |
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### Downstream Use |
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- Fine-tuning for industry-specific applications like healthcare, finance, or legal tech. |
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- Integrating into specialized chatbots, virtual assistants, or automated knowledge bases. |
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### Out-of-Scope Use |
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- Generating non-English responses (English-only capability). |
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- Handling non-technical, unrelated queries outside the AI domain. |
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## Bias, Risks, and Limitations |
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- **Bias:** Trained on domain-specific datasets, the model may exhibit biases toward AI-related terminologies or fail to generalize well in other domains. |
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- **Risks:** May generate incorrect or misleading information if the query is ambiguous or goes beyond the model’s scope. |
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- **Limitations:** May struggle with highly complex or nuanced queries not covered in its fine-tuning data. |
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### Recommendations |
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- For critical or high-stakes applications, it’s recommended to use the model with human oversight. |
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- Regularly update the fine-tuning datasets to ensure alignment with the latest research and advancements in AI. |
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--- |
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## How to Get Started |
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To use the model, install the `transformers` library and use the following code snippet: |
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```python |
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from transformers import pipeline |
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# Load the model |
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model = pipeline("text2text-generation", model="TinyLlama/TinyLlama-1.1B-Chat-v1.0") |
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# Generate a response |
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output = model("What is the difference between supervised and unsupervised learning?") |
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print(output) |
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