| --- |
| library_name: peft |
| license: apache-2.0 |
| base_model: meta-llama/Llama-2-7b-hf |
| tags: |
| - resume-screening |
| - hr-tech |
| - llama2 |
| - lora |
| - peft |
| - fine-tuned |
| --- |
| |
| # 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) |
| |
| 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 |
| - 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. |
