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--- |
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library_name: peft |
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license: apache-2.0 |
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base_model: meta-llama/Llama-2-7b-hf |
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tags: |
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- resume-screening |
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- hr-tech |
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- llama2 |
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- lora |
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- peft |
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- fine-tuned |
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--- |
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# Advanced Resume Screening Model |
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## Model Description |
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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. |
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- **Developed by:** kiritps |
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- **Model type:** Causal Language Model (LoRA Fine-tuned) |
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- **Language(s):** English |
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- **License:** Apache 2.0 |
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- **Finetuned from model:** meta-llama/Llama-2-7b-hf |
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## Model Sources |
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- **Repository:** https://huggingface.co/kiritps/Advanced-resume-screening |
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## Uses |
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### Direct Use |
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This model is designed for HR professionals and recruitment systems to: |
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- Analyze and screen resumes automatically |
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- Extract key qualifications and skills |
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- Provide structured candidate assessments |
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- Filter candidates based on specific criteria |
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- Generate summaries of candidate profiles |
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### Downstream Use |
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The model can be integrated into: |
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- Applicant Tracking Systems (ATS) |
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- HR management platforms |
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- Recruitment automation tools |
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- Candidate matching systems |
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### Out-of-Scope Use |
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- Should not be used as the sole decision-maker in hiring processes |
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- Not intended for discriminatory screening based on protected characteristics |
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- Not suitable for general-purpose text generation outside of resume/HR context |
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## How to Get Started with the Model |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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from peft import PeftModel |
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Load base model and tokenizer |
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base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf") |
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tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf") |
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Load LoRA adapter |
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model = PeftModel.from_pretrained(base_model, "kiritps/Advanced-resume-screening") |
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Example usage |
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prompt = "Analyze this resume and provide key qualifications: [RESUME TEXT HERE]" |
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inputs = tokenizer(prompt, return_tensors="pt") |
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outputs = model.generate(**inputs, max_length=512, temperature=0.7) |
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response = tokenizer.decode(outputs, skip_special_tokens=True) |
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text |
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## Training Details |
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### Training Data |
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The model was fine-tuned on a curated dataset of resume-response pairs, designed to teach the model how to: |
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- Extract relevant information from resumes |
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- Provide structured analysis of candidate qualifications |
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- Generate appropriate screening responses |
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### Training Procedure |
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#### Training Hyperparameters |
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- **Training regime:** 4-bit quantization with bfloat16 mixed precision |
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- **LoRA rank:** 64 |
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- **LoRA alpha:** 16 |
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- **Learning rate:** 2e-4 |
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- **Batch size:** 4 |
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- **Gradient accumulation steps:** 4 |
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- **Training epochs:** Multiple checkpoints saved (3840, 4320, 4800, 5280, 5760 steps) |
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#### Quantization Configuration |
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- **Quantization method:** bitsandbytes |
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- **Load in 4bit:** True |
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- **Quantization type:** nf4 |
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- **Double quantization:** True |
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- **Compute dtype:** bfloat16 |
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## Bias, Risks, and Limitations |
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### Limitations |
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- Model responses should be reviewed by human recruiters |
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- May exhibit biases present in training data |
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- Performance may vary across different industries or job types |
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- Requires careful prompt engineering for optimal results |
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### Recommendations |
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- Use as a screening aid, not a replacement for human judgment |
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- Regularly audit outputs for potential bias |
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- Combine with diverse evaluation methods |
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- Ensure compliance with local employment laws and regulations |
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## Technical Specifications |
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### Model Architecture |
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- **Parameter Count:** ~7B parameters (base) + LoRA adapters |
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- **Quantization:** 4-bit NF4 quantization |
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### Compute Infrastructure |
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#### Hardware |
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- GPU training environment |
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- Compatible with consumer and enterprise GPUs |
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#### Software |
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- **Framework:** PyTorch |
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- **PEFT Version:** 0.6.2 |
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- **Transformers:** Latest compatible version |
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- **Quantization:** bitsandbytes |
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## Training Procedure |
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The following `bitsandbytes` quantization config was used during training: |
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- quant_method: bitsandbytes |
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- load_in_8bit: False |
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- load_in_4bit: True |
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- llm_int8_threshold: 6.0 |
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- llm_int8_skip_modules: None |
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- llm_int8_enable_fp32_cpu_offload: False |
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- llm_int8_has_fp16_weight: False |
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- bnb_4bit_quant_type: nf4 |
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- bnb_4bit_use_double_quant: True |
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- bnb_4bit_compute_dtype: bfloat16 |
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### Framework Versions |
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- PEFT 0.6.2 |
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- Transformers (compatible version) |
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- PyTorch (latest stable) |
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- bitsandbytes (for quantization) |
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## Model Card Authors |
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kiritps |
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## Model Card Contact |
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For questions or issues regarding this model, please open an issue in the model repository. |
