ResumeScreener

Model Card for HireSense Resume Parser LoRA

Model Details

Model Description

HireSense Resume Parser LoRA is a fine-tuned adapter model built on top of Qwen3-4B-Instruct using QLoRA and supervised fine-tuning (SFT). The model is designed to extract structured JSON information from resumes for downstream recruitment and candidate-job matching workflows.

The model converts raw resume text into a consistent structured schema containing:

• Personal information
• Skills
• Education
• Work experience
• Projects
• Certifications

This model is intended to be used as a component in AI-powered hiring pipelines and resume analysis systems.

• Developed by: Rohit BK
• Model type: Causal Language Model (LoRA Adapter)
• Language(s): English
• License: Apache-2.0
• Finetuned from model: unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit

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Model Sources

• Base Model: Qwen3-4B-Instruct
• Frameworks: Transformers, PEFT, TRL, Unsloth

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Uses

Direct Use

This model is intended for:

• Resume parsing
• Structured information extraction
• Candidate profile generation
• Resume-to-JSON conversion
• Recruitment automation systems

Example output schema:

{
  "name": "John Doe",
  "email": "[email protected]",
  "phone": "9876543210",
  "skills": ["Python", "React", "SQL"],
  "education": [
    {
      "degree": "B.Tech",
      "institution": "XYZ University",
      "year": "2025"
    }
  ]
}

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Downstream Use

The model can be integrated into:

• Applicant Tracking Systems (ATS)
• Resume ranking systems
• Semantic candidate matching pipelines
• Recruitment copilots
• Hiring analytics dashboards

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Out-of-Scope Use

This model is NOT intended for:

• Final hiring decisions
• Automated candidate rejection without human review
• Personality assessment
• Predicting candidate success
• Sensitive demographic inference

Human oversight is strongly recommended.

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Bias, Risks, and Limitations

The model may:

• Produce incorrect or incomplete JSON
• Miss information in poorly formatted resumes
• Exhibit biases inherited from training data
• Struggle with multilingual resumes
• Perform inconsistently on highly creative resume layouts

The model should not be used as the sole decision-maker in hiring processes.

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Recommendations

Users should:

• Validate generated outputs before use
• Use human review for hiring decisions
• Combine the model with rule-based validation systems
• Avoid relying solely on generated scores or rankings

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How to Get Started with the Model

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model_id = "Qwen/Qwen3-4B-Instruct"
adapter_id = "YOUR_USERNAME/HireSense-ResumeParser-LoRA"

tokenizer = AutoTokenizer.from_pretrained(base_model_id)

base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    device_map="auto"
)

model = PeftModel.from_pretrained(base_model, adapter_id)

prompt = """
Extract structured JSON information from the following resume.

Resume:
John Doe
Python Developer
Skills: Python, React, SQL
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=256
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Training Details

Training Data

The model was trained on structured resume-to-JSON instruction pairs containing:

• Resume text
• Extraction prompts
• Structured JSON outputs

Training data included synthetic and manually curated resume samples.

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Training Procedure

The model was fine-tuned using:

• QLoRA
• Supervised Fine-Tuning (SFT)
• 4-bit quantization
• PEFT adapters

Training Hyperparameters

• Training regime: bf16 mixed precision
• Fine-tuning method: QLoRA
• Quantization: 4-bit NF4
• Optimizer: AdamW
• Frameworks: Transformers + TRL + Unsloth

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Evaluation

Metrics

The model was evaluated qualitatively on:

• JSON validity
• Field extraction accuracy
• Structural consistency
• Hallucination frequency

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Results

The model demonstrated:

• Consistent JSON generation
• Good extraction performance on structured resumes
• Improved formatting consistency compared to the base model

Performance may degrade on:

• Image-based resumes
• Multi-column layouts
• Highly unstructured resumes

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Environmental Impact

• Hardware Type: NVIDIA GPU
• Training Framework: Unsloth
• Quantization: 4-bit QLoRA

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Technical Specifications

Model Architecture and Objective

This model uses:

• Qwen3-4B-Instruct as the base model
• LoRA adapters for parameter-efficient fine-tuning
• Causal language modeling objective

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Citation

BibTeX

@misc{hiresense2026,
  title={HireSense Resume Parser LoRA},
  author={Rohit BK},
  year={2026},
  publisher={Hugging Face}
}

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Model Card Authors

Rohit BK

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Model Card Contact

For questions or collaboration inquiries, please contact through Hugging Face or GitHub.

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Framework versions

• PEFT 0.19.1
• Transformers
• TRL
• Unsloth