SK0988/phi3-hr-assistant

Phi-3 HR Assistant

This model is a fine-tuned version of microsoft/Phi-3-mini-4k-instruct using LoRA (Low-Rank Adaptation) for HR policy assistance.

Model Details

• Base Model: microsoft/Phi-3-mini-4k-instruct
• Fine-tuning Method: LoRA (r=8, alpha=16, dropout=0.05)
• Task: HR Policy Question Answering
• Language: English
• Model Size: ~3.8B parameters

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "SK0988/phi3-hr-assistant",
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("SK0988/phi3-hr-assistant")

# Generate HR response
def ask_hr_question(question):
    prompt = f'''You are the company's HR Helpdesk assistant. Answer HR policy questions accurately.

### Human:
{question}

### Assistant:
'''
    
    inputs = tokenizer(prompt, return_tensors="pt")
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=200,
            do_sample=False,
            pad_token_id=tokenizer.eos_token_id
        )
    
    response = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
    return response.strip()

# Example usage
question = "What is the leave policy for permanent employees?"
answer = ask_hr_question(question)
print(answer)

API Usage

# Using Hugging Face Inference API
import requests

API_URL = "https://api-inference.huggingface.co/models/SK0988/phi3-hr-assistant"
headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}

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

# Ask HR question
output = query({
    "inputs": "What is the maternity leave policy?",
    "parameters": {"max_new_tokens": 200}
})
print(output)

Training Details

• Training Data: HR policies and Q&A pairs
• Training Method: Supervised Fine-tuning with LoRA
• Target Modules: q_proj, k_proj, v_proj, o_proj
• Training Steps: 1000
• Base Model: microsoft/Phi-3-mini-4k-instruct

Use Cases

• HR policy inquiries
• Leave policy questions
• Travel allowance information
• Certification reimbursement queries
• General HR assistance

Limitations

• Model responses should be verified against current HR policies
• Not suitable for sensitive HR decisions without human oversight
• May require additional context for complex policy questions
• Responses are based on training data and may not reflect latest policy changes

Ethical Considerations

• This model is designed for HR assistance only
• Should not be used for discriminatory purposes
• Human oversight recommended for important decisions
• Ensure compliance with local employment laws

Model Performance

The model has been fine-tuned specifically for HR-related queries and shows good performance on:

• Leave policy questions
• Travel allowance inquiries
• Certification reimbursement
• General HR procedures

Citation

If you use this model, please cite:

@misc{phi3-hr-assistant,
  title={Phi-3 HR Assistant},
  author={SK0988},
  year={2024},
  publisher={Hugging Face},
  url={https://huggingface.co/SK0988/phi3-hr-assistant}
}