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

python

# 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}
}