Harikrishnan46624/finetuned_llama2-1.1b-chat

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.

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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

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Model Sources

• Repository: Fine-Tuning Notebook on GitHub
• Demo: [Demo Link to be Added]

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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.

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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.

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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.

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How to Get Started

To use the model, install the transformers library and use the following code snippet:

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)