| --- |
| base_model: meta-llama/Llama-3.2-3B-Instruct |
| library_name: peft |
| tags: |
| - hr-analytics |
| - attrition-risk |
| - reasoning-traces |
| - autoscientist |
| - llama-3.2 |
| - peft |
| license: apache-2.0 |
| datasets: |
| - CorpIntel-Attrition-Reasoning-v1 |
| language: |
| - en |
| pipeline_tag: text-generation |
| --- |
| |
| # CorpIntel-HR-Agent |
|
|
| `CorpIntel-HR-Agent` is an instruction-tuned 3B parameter model fine-tuned on `meta-llama/Llama-3.2-3B-Instruct` using PEFT (LoRA). The model is specifically engineered to evaluate complex, multi-variable employee telemetry (commute friction, salary hikes, overtime, job satisfaction, and career stagnation) to perform attrition risk modeling and generate structured **Managerial Intervention Plans**. |
|
|
| Unlike standard binary classifiers that output a simple "Yes/No" risk score, `CorpIntel-HR-Agent` generates explicit step-by-step reasoning traces detailing *why* an employee is a flight risk and *what* specific managerial steps can retain them. |
|
|
| ## Model Details |
|
|
| ### Model Description |
|
|
| - **Developed by:** Asad Ullah Dogar |
| - **Model type:** PEFT / LoRA Adapter (Causal LM) |
| - **Language(s):** English (en) |
| - **License:** Apache-2.0 |
| - **Finetuned from model:** `meta-llama/Llama-3.2-3B-Instruct` |
| - **Platform:** Adaption Labs AutoScientist Engine |
|
|
| ### Model Sources |
|
|
| - **Dataset:** `CorpIntel-Attrition-Reasoning-v1` |
| - **Base Architecture:** Llama 3.2 3B Instruct |
|
|
| --- |
|
|
| ## Uses |
|
|
| ### Direct Use |
| * **HR Analytics & Decision Support:** Evaluating employee telemetry to identify hidden burnout and retention risks. |
| * **Reasoning Generation:** Synthesizing multi-variable data (e.g., long commute + low salary hike + high overtime) into actionable narrative diagnostics. |
| * **Retention Strategy Generation:** Crafting tailored Managerial Intervention Plans for HR Business Partners and regional leaders. |
|
|
| ### Out-of-Scope Use |
| * **Automated Termination/Hiring:** This model is designed purely as an analytical decision-support tool. It must **not** be used for fully automated HR decisions without human oversight. |
|
|
| --- |
|
|
| ## Bias, Risks, and Limitations |
|
|
| * **Domain Specificity:** Trained on corporate HR telemetry schemas. Metrics using significantly different column formats may require input rephrasing or context mapping. |
| * **Decision Support Only:** The model outputs recommendations and reasoning traces based on input parameters; human HR expertise is required to validate intervention strategies. |
|
|
| --- |
|
|
| ## How to Get Started with the Model |
|
|
| ```python |
| import torch |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| from peft import PeftModel |
| |
| base_model_id = "meta-llama/Llama-3.2-3B-Instruct" |
| adapter_id = "CorpIntel-HR-Agent" # Replace with your Hugging Face username/repo |
| |
| tokenizer = AutoTokenizer.from_pretrained(base_model_id) |
| base_model = AutoModelForCausalLM.from_pretrained( |
| base_model_id, |
| torch_dtype=torch.bfloat16, |
| device_map="auto" |
| ) |
| |
| model = PeftModel.from_pretrained(base_model, adapter_id) |
| |
| prompt = """<|start_header_id|>system<|end_header_id|> |
| You are an elite HR Business Partner AI. Your objective is to evaluate heterogeneous employee telemetry to model attrition risk and generate a Managerial Intervention Plan.<|eot_id|> |
| <|start_header_id|>user<|end_header_id|> |
| Evaluate Candidate: Sales Executive | Travel: Frequently | Distance From Home: 24 miles | Monthly Income: 3200 | OverTime: Yes | JobSatisfaction: 1 | YearsSinceLastPromotion: 4<|eot_id|> |
| <|start_header_id|>assistant<|end_header_id|>""" |
| |
| inputs = tokenizer(prompt, return_tensors="pt").to("cuda") |
| outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.3) |
| print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
