adaption_hr_management_qa

language:

• en base_model:
• meta-llama/Llama-3.3-70B-Instruct-Reference license: other tags:
• human-resources
• hr-management
• question-answering
• text-generation
• adaptive-data
• autosscientist
• lora pipeline_tag: text-generation credit: "Adaptive Data by Adaption Labs"

---

HR Management QA — Adaptive Data

A focused Human Resources dataset designed to improve AI responses to practical HR questions and workplace scenarios.

Overview

This dataset contains 113 HR-focused examples covering recruitment, employee development, workplace policies, attendance, maternity leave, performance management, career development, and employee support.

The dataset was enhanced using Adaption Labs Adaptive Data and evaluated through AutoScientist.

Dataset Summary

Adaptive Data Results

Quality improvement: approximately 54%

AutoScientist Evaluation

The adapted dataset was used for model training and evaluation.

The results show a substantial improvement in HR-focused performance after dataset adaptation.

Example

Question:
What strategies can organizations use to create personalized
skill development plans?

Answer:
Organizations can assess an employee's current skills, identify
career goals, recommend relevant training and mentoring, establish
measurable milestones, and review progress regularly.
credit: "Adaptive Data by Adaption Labs"


A LORA adapter for `meta-llama/Llama-3.3-70B-Instruct-Reference`. This model was trained with SFT using [Adaption](https://adaptionlabs.ai)'s AutoScientist on the hr_management_qa dataset.

![Training metrics](training-metrics.png)

### AutoScientist Config

```json
{
  "job_id": "3396a5fc-bbc4-4fd0-a331-c447b80d9660",
  "training_experiment_id": "ba32d529-700d-4685-9b4f-c6318459deac",
  "original_model_name": "meta-llama/Llama-3.3-70B-Instruct-Reference",
  "trained_model_name": "adaption_hr_management_qa",
  "training_method": "sft",
  "training_type": "lora",
  "data_format": "chat",
  "hyperparams": {
    "lora": "true",
    "lora_r": 64,
    "n_evals": 5,
    "n_epochs": 3,
    "batch_size": "max",
    "lora_alpha": 128,
    "lora_dropout": 0,
    "min_lr_ratio": 0.1,
    "warmup_ratio": 0.05,
    "weight_decay": 0.02,
    "learning_rate": 0.0001,
    "max_grad_norm": 1,
    "base_model_size": "70B",
    "train_on_inputs": "false",
    "training_method": "sft",
    "lr_scheduler_type": "linear",
    "scheduler_num_cycles": 0.5,
    "lora_trainable_modules": "all-linear"
  }
}

Training Data

The model was trained on 1,254 rows of adapted data with the following domain distribution: hr (91%), legal (2%), corporate-business (2%), career-workplace (1%), governance (1%), personal-finance (1%), technology (1%), academic-education (0%), marketing (0%), data-analysis-visualization (0%), science (0%), personal-growth (0%), architecture-design (0%).

Model Evaluation

The model was evaluated on an in-distribution held-out test set as well as a broader domain-specific test set to measure generalization.

How to use

pip install torch transformers peft

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

BASE = "meta-llama/Llama-3.3-70B-Instruct-Reference"
ADAPTER = "<this-repo-id>"

device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float32 if device == "cpu" else torch.bfloat16

base = AutoModelForCausalLM.from_pretrained(BASE, dtype=dtype).to(device)
model = PeftModel.from_pretrained(base, ADAPTER)
# Optional: merge the LoRA weights into the base for faster inference
model = model.merge_and_unload()
model.eval()

tokenizer = AutoTokenizer.from_pretrained(BASE)
messages = [{"role": "user", "content": "Hello!"}]
text = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(device)

with torch.inference_mode():
    out = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))