| --- |
| license: mit |
| base_model: microsoft/Phi-3-mini-4k-instruct |
| library_name: transformers |
| pipeline_tag: text-generation |
| tags: |
| - phi3 |
| - lora |
| - hr-assistant |
| - fine-tuned |
| - text-generation |
| language: |
| - en |
| inference: true |
| --- |
| |
| # Phi-3 HR Assistant |
|
|
| This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/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 |
|
|
| ```python |
| 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} |
| } |
| ``` |
