CoolFace
Modelpublic

kiritps/Advanced-resume-screening

sourceHugging Faceapache-2.0updated 1y agoView on Hugging Face
2likes8downloads
Model Card

Advanced Resume Screening Model

Model Description

This is a LoRA (Low-Rank Adaptation) fine-tuned version of Llama-2-7B specifically optimized for resume screening and candidate evaluation tasks. The model can analyze resumes, extract key information, and provide structured assessments of candidate qualifications.

  • —Developed by: kiritps
  • —Model type: Causal Language Model (LoRA Fine-tuned)
  • —Language(s): English
  • —License: Apache 2.0
  • —Finetuned from model: meta-llama/Llama-2-7b-hf

Model Sources

  • —Repository: https://huggingface.co/kiritps/Advanced-resume-screening

Uses

Direct Use

This model is designed for HR professionals and recruitment systems to:

  • —Analyze and screen resumes automatically
  • —Extract key qualifications and skills
  • —Provide structured candidate assessments
  • —Filter candidates based on specific criteria
  • —Generate summaries of candidate profiles

Downstream Use

The model can be integrated into:

  • —Applicant Tracking Systems (ATS)
  • —HR management platforms
  • —Recruitment automation tools
  • —Candidate matching systems

Out-of-Scope Use

  • —Should not be used as the sole decision-maker in hiring processes
  • —Not intended for discriminatory screening based on protected characteristics
  • —Not suitable for general-purpose text generation outside of resume/HR context

How to Get Started with the Model

from transformers import AutoTokenizer, AutoModelForCausalLM from peft import PeftModel

Load base model and tokenizer basemodel = AutoModelForCausalLM.frompretrained("meta-llama/Llama-2-7b-hf") tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")

Load LoRA adapter model = PeftModel.frompretrained(basemodel, "kiritps/Advanced-resume-screening")

Example usage prompt = "Analyze this resume and provide key qualifications: [RESUME TEXT HERE]" inputs = tokenizer(prompt, returntensors="pt") outputs = model.generate(**inputs, maxlength=512, temperature=0.7) response = tokenizer.decode(outputs, skipspecialtokens=True)

text

Training Details

Training Data

The model was fine-tuned on a curated dataset of resume-response pairs, designed to teach the model how to:

  • —Extract relevant information from resumes
  • —Provide structured analysis of candidate qualifications
  • —Generate appropriate screening responses

Training Procedure

Training Hyperparameters
  • —Training regime: 4-bit quantization with bfloat16 mixed precision
  • —LoRA rank: 64
  • —LoRA alpha: 16
  • —Learning rate: 2e-4
  • —Batch size: 4
  • —Gradient accumulation steps: 4
  • —Training epochs: Multiple checkpoints saved (3840, 4320, 4800, 5280, 5760 steps)
Quantization Configuration
  • —Quantization method: bitsandbytes
  • —Load in 4bit: True
  • —Quantization type: nf4
  • —Double quantization: True
  • —Compute dtype: bfloat16

Bias, Risks, and Limitations

Limitations

  • —Model responses should be reviewed by human recruiters
  • —May exhibit biases present in training data
  • —Performance may vary across different industries or job types
  • —Requires careful prompt engineering for optimal results

Recommendations

  • —Use as a screening aid, not a replacement for human judgment
  • —Regularly audit outputs for potential bias
  • —Combine with diverse evaluation methods
  • —Ensure compliance with local employment laws and regulations

Technical Specifications

Model Architecture

  • —Parameter Count: ~7B parameters (base) + LoRA adapters
  • —Quantization: 4-bit NF4 quantization

Compute Infrastructure

Hardware
  • —GPU training environment
  • —Compatible with consumer and enterprise GPUs
Software
  • —Framework: PyTorch
  • —PEFT Version: 0.6.2
  • —Transformers: Latest compatible version
  • —Quantization: bitsandbytes

Training Procedure

The following bitsandbytes quantization config was used during training:

  • —quant_method: bitsandbytes
  • —loadin8bit: False
  • —loadin4bit: True
  • —llmint8threshold: 6.0
  • —llmint8skip_modules: None
  • —llmint8enablefp32cpu_offload: False
  • —llmint8hasfp16weight: False
  • —bnb4bitquant_type: nf4
  • —bnb4bitusedoublequant: True
  • —bnb4bitcompute_dtype: bfloat16

Framework Versions

  • —PEFT 0.6.2
  • —Transformers (compatible version)
  • —PyTorch (latest stable)
  • —bitsandbytes (for quantization)

Model Card Authors

kiritps

Model Card Contact

For questions or issues regarding this model, please open an issue in the model repository.