Survey Finetuned TinyLlama 1.1B Chat V1.0

🚀 aryashah00/survey-finetuned-TinyLlama-1.1B-Chat-v1.0

This model is a fine - tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0, which is optimized for generating synthetic survey responses across multiple domains. It can generate responses reflecting specific personas, offering high - quality synthetic data for various survey needs.

🚀 Quick Start

This model is specifically designed for generating synthetic survey responses from different personas. To get the best results, you need to provide a detailed persona description and a specific survey question.

✨ Features

• Domain - Adaptable: Optimized for generating synthetic survey responses across multiple domains.
• Persona - Aware: Instruction - tuned to generate responses reflecting specific personas.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("aryashah00/survey-finetuned-TinyLlama-1.1B-Chat-v1.0", device_map="auto", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("aryashah00/survey-finetuned-TinyLlama-1.1B-Chat-v1.0", trust_remote_code=True)

# Define persona and question
persona = "A nurse who educates the child about modern medical treatments and encourages a balanced approach to healthcare"
question = "How often was your pain well controlled during this hospital stay?"

# Prepare prompts
system_prompt = f"You are embodying the following persona: {persona}"
user_prompt = f"Survey Question: {question}\n\nPlease provide your honest and detailed response to this question."

# Create message format
messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": user_prompt}
]

# Apply chat template
input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

# Tokenize
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(model.device)

# Generate response
import torch
with torch.no_grad():
    output_ids = model.generate(
        input_ids=input_ids,
        max_new_tokens=256,
        temperature=0.7,
        top_p=0.9,
        do_sample=True
    )

# Decode
output = tokenizer.decode(output_ids[0], skip_special_tokens=True)

# Extract just the generated response
response_start = output.find(input_text) + len(input_text)
generated_response = output[response_start:].strip()

print(f"Generated response: {generated_response}")

Advanced Usage

import requests

API_URL = "https://api-inference.huggingface.co/models/aryashah00/survey-finetuned-TinyLlama-1.1B-Chat-v1.0"
headers = {"Authorization": "Bearer YOUR_API_KEY"}

def query(payload):
    response = requests.post(API_URL, headers=headers, json=payload)
    return response.json()

messages = [
    {"role": "system", "content": "You are embodying the following persona: A nurse who educates the child about modern medical treatments and encourages a balanced approach to healthcare"},
    {"role": "user", "content": "Survey Question: How often was your pain well controlled during this hospital stay?\n\nPlease provide your honest and detailed response to this question."}
]

output = query({"inputs": messages})
print(output)

📚 Documentation

Model Description

This model is a fine - tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 optimized for generating synthetic survey responses across multiple domains. It has been instruction - tuned using a custom dataset of survey responses, with each response reflecting a specific persona.

Training Data

Training Details

• Base Model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
• Training Method: Parameter - Efficient Fine - Tuning with LoRA
• LoRA Parameters: r = 16, alpha = 32, dropout = 0.05
• Training Setup:
  • Batch Size: 8
  • Learning Rate: 0.0002
  • Epochs: 5

🔧 Technical Details

• Base Model: The model is based on TinyLlama/TinyLlama-1.1B-Chat-v1.0.
• Fine - Tuning: It uses Parameter - Efficient Fine - Tuning with LoRA, with specific parameters (r = 16, alpha = 32, dropout = 0.05) to adapt the base model to generate synthetic survey responses.

📄 License

This model follows the license of the base model TinyLlama/TinyLlama-1.1B-Chat-v1.0.

⚠️ Important Note

• The model is optimized for survey response generation and may not perform well on other tasks.
• Response quality depends on the clarity and specificity of the persona and question.
• The model may occasionally generate responses that don't fully align with the given persona.