README.md

---
pipeline_tag: text-generation
license: mit
datasets:
- data.jsonl
language:
- en
base_model:
- EleutherAI/gpt-neo-125m
new_version: v1.0
library_name: transformers
tags:
- GPT-Neo
- Fine-Tuned
- Chatbot
- Text Generation
- Abhinav Academy
- NLP
---
# Abhinav Academy Chatbot

A fine-tuned language model designed to answer questions about Abhinav Academy's courses, facilities, and services. This model is based on EleutherAI's GPT-Neo 1.3B and has been specifically trained to provide accurate information about the institution.

## Model Details

- **Base Model**: EleutherAI/gpt-neo-1.3B
- **Training Dataset**: Custom dataset of question-answer pairs about Abhinav Academy
- **Task**: Instruction-following for educational institution information
- **Primary Use Case**: Answering student and parent queries about Abhinav Academy

## Use Cases

This model is designed to:

- Answer questions about course offerings (JEE, NEET, MHT-CET preparation)
- Provide information about faculty and facilities
- Explain admission requirements and processes
- Share details about extracurricular activities
- Address queries about fees, scholarships, and logistical details

## Example Usage

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load model and tokenizer
model_name = "accesscreate012/abhinav-chatbot"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Function to generate responses
def generate_response(instruction, max_new_tokens=150):
    prompt = f"Instruction: {instruction}\nResponse:"
    
    # Tokenize the input
    inputs = tokenizer(prompt, return_tensors="pt")
    
    # Generate with improved parameters
    outputs = model.generate(
        **inputs,
        max_new_tokens=max_new_tokens,
        num_beams=5,
        no_repeat_ngram_size=3,
        early_stopping=True,
        top_p=0.92,
        temperature=0.7,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )
    
    # Process and clean the output
    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    
    # Extract only the response part
    if "Response:" in generated_text:
        response = generated_text.split("Response:")[1].strip()
    else:
        response = generated_text.replace(prompt, "").strip()
    
    return response

# Example queries
queries = [
    "What courses do you offer for JEE preparation?",
    "Tell me about your NEET coaching program",
    "Where is Abhinav Academy located?",
    "Do you have hostel facilities?"
]

# Generate and print responses
for query in queries:
    print(f"Q: {query}")
    print(f"A: {generate_response(query)}")
    print()
```

## Sample Interactions

**Q: What courses does Abhinav Academy offer?**  
A: We offer MHT-CET, JEE, NEET preparation, as well as supplementary courses like English Communication, Personality Development, and Foreign Languages (German/French).

**Q: Does Abhinav Academy offer NEET coaching?**  
A: Yes, we offer NEET coaching with expert faculty and structured study materials. Our 24-month NEET Preparation program emphasizes NCERT-based learning and practical lab sessions for Biology, Physics, and Chemistry.

**Q: What are the admission requirements for Abhinav Academy?**  
A: Admission requirements vary by program. Generally, students must pass an entrance test and interview. Competitive courses require strong academics.

## Limitations

- The model is specialized for information about Abhinav Academy only
- It may not provide accurate information about other educational institutions
- Responses are based on training data and may not reflect real-time changes to curriculum or policies
- The model should be regularly updated as the institution's offerings evolve

## Training Methodology

This model was fine-tuned using a supervised approach on a custom dataset containing question-answer pairs about Abhinav Academy. The training process focused on:

- Preserving factual accuracy about course offerings
- Maintaining consistent formatting and tone
- Optimizing for natural-sounding responses
- Handling variations in question phrasing

## Integration

This model can be integrated into:

- The academy's official website as a chat assistant
- Mobile applications for student support
- SMS or WhatsApp-based query systems
- Internal student support systems

## License

This model is provided for educational purposes. Please contact Abhinav Academy for commercial use.

## Contact

For questions or feedback about this model, please reach out to [CONTACT_EMAIL].