CorpIntel-HR-Agent

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

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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.

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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.

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How to Get Started with the Model

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))