asadullahdogarr

CorpIntel-HR-Agent

Deploy Dedicated

README

License: apache-2.0

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))

Available on FriendliAI

Dedicated Endpoints

Run this model inference on single tenant GPU with unmatched speed and reliability at scale.

Learn more

Container

Run this model inference with full control and performance in your environment.

Learn more

Model Details

Model Provider

asadullahdogarr

Model Tree

Base

meta-llama/Llama-3.2-3B-Instruct

Adapter

this model

Input Modalities

Text

Output Modalities

Text

Supported Functionality

Dedicated Endpoints

Container

Explore FriendliAI today

Get started Talk to an engineer

README

License: apache-2.0

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))