survey-finetuned-Mistral-7B-Instruct-v0.3

aryashah00/survey-finetuned-Mistral-7B-Instruct-v0.3

Model Description

This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.3 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

• Dataset Size: ~3,000 examples
• Domains: 10 domains including healthcare, education, etc.
• Format: ChatML instruction format with system and user prompts

Training Details

• Base Model: mistralai/Mistral-7B-Instruct-v0.3
• 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

Usage

This model is specifically designed for generating synthetic survey responses from different personas. It works best when provided with:

1. A detailed persona description
2. A specific survey question

Python Example

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("aryashah00/survey-finetuned-Mistral-7B-Instruct-v0.3", device_map="auto", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("aryashah00/survey-finetuned-Mistral-7B-Instruct-v0.3", 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}}")

Inference API Example

import requests

API_URL = "https://api-inference.huggingface.co/models/aryashah00/survey-finetuned-Mistral-7B-Instruct-v0.3"
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)

Limitations

• 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

License

This model follows the license of the base model mistralai/Mistral-7B-Instruct-v0.3.