asadullahdogarr
/

CorpIntel-HR-Agent

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 ---
-base_model: togethercomputer/Meta-Llama-3.2-3B-Instruct-Reference__TOG__FT
 library_name: peft
 ---
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-### Framework versions
-- PEFT 0.15.1

 ---
+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))