kiritps/Advanced-resume-screening

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 base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf") tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")

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

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

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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
• load_in_8bit: False
• load_in_4bit: True
• llm_int8_threshold: 6.0
• llm_int8_skip_modules: None
• llm_int8_enable_fp32_cpu_offload: False
• llm_int8_has_fp16_weight: False
• bnb_4bit_quant_type: nf4
• bnb_4bit_use_double_quant: True
• bnb_4bit_compute_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.