---
library_name: transformers
tags:
- resume
- career
- job-search
- interview
- cover-letter
- professional-writing
- fine-tuned
- lora
- career-guidance
- job-application
license: apache-2.0
datasets:
- MikePfunk28/resume-training-dataset
language:
- en
pipeline_tag: text-generation
widget:
- text: "Human: How do I write a professional resume summary?\n\nAssistant:"
  example_title: "Resume Summary Help"
- text: "Human: What skills should I highlight for a software engineer?\n\nAssistant:"
  example_title: "Skills Guidance"
- text: "Human: Help me write a cover letter for a data scientist position\n\nAssistant:"
  example_title: "Cover Letter Help"
---

# Model Card for resume-ai-assistant

A specialized AI assistant fine-tuned for resume writing, career guidance, and job search support based on GPT-Neo 1.3B.

## Model Details

### Model Description

This model is a fine-tuned for specifically optimized for resume and career-related tasks. Using LoRA (Low-Rank Adaptation) fine-tuning, it provides professional guidance on resume writing, cover letters, interview preparation, and career development while maintaining the base model's strong language generation capabilities.

- **Developed by:** KIRIT P S
- **Model type:** Causal Language Model (Decoder)
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Specialized for:** Resume writing, career guidance, job search assistance

### Model Sources

- **Training Dataset:** [MikePfunk28/resume-training-dataset](https://huggingface.co/datasets/MikePfunk28/resume-training-dataset)
- **Fine-tuning Method:** LoRA (Low-Rank Adaptation)

## Uses

### Direct Use

The model is designed for direct use in career-related applications:

- **Resume Writing:** Generate professional summaries, describe work experience, highlight relevant skills
- **Cover Letter Creation:** Write compelling cover letters tailored to specific job applications
- **Interview Preparation:** Practice responses to common behavioral and technical interview questions
- **Career Advice:** Receive guidance on career transitions, skill development, and job search strategies
- **Professional Communication:** Improve LinkedIn profiles, networking messages, and professional correspondence

### Downstream Use

This model can be integrated into:

- Career counseling platforms and job search websites
- HR tools for resume screening and candidate assessment
- Educational platforms for career development courses
- Chatbots and virtual assistants focused on career guidance
- Professional writing tools and browser extensions

### Out-of-Scope Use

- **General-purpose text generation:** Not optimized for non-career related content
- **Academic writing:** Not specifically trained for research papers or academic content
- **Creative writing:** Limited capability for fiction, poetry, or creative storytelling
- **Technical documentation:** Not specialized for software documentation or technical manuals
- **Legal or medical advice:** Should not be used for professional legal or medical guidance

## Bias, Risks, and Limitations

**Potential Biases:**
- May reflect biases present in traditional resume writing and hiring practices
- Could favor certain industries or job roles over others based on training data
- May inadvertently perpetuate gender, racial, or cultural biases in professional advice

**Technical Limitations:**
- Context window limited to 512 tokens for optimal performance
- Performance may degrade for highly specialized or niche career fields
- Generated content requires human review and editing
- May not reflect the most current job market trends or industry changes

**Risk Considerations:**
- Users should not rely solely on AI-generated content for critical job applications
- Output quality may vary depending on input specificity and context
- May not account for individual circumstances or local job market conditions

### Recommendations

- **Always review and edit** AI-generated content before using in actual applications
- **Combine with human expertise** such as career counselors or industry professionals
- **Verify information** against current industry standards and job requirements
- **Consider cultural context** and local job market practices
- **Use as a starting point** rather than a final solution for career documents

## How to Get Started with the Model
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("kiritps/resume-ai-assistant")
model = AutoModelForCausalLM.from_pretrained(
"kiritps/resume-ai-assistant",
torch_dtype=torch.float16,
device_map="auto"
)

Example usage
prompt = "Human: How do I describe my leadership experience on my resume?\n\nAssistant:"
inputs = tokenizer(prompt, return_tensors="pt")

Generate response
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=150,
temperature=0.7,
do_sample=True,
pad_token_id=tokenizer.eos_token_id
)

response = tokenizer.decode(outputs, skip_special_tokens=True)
print(response[len(prompt):])


## Training Details

### Training Data

The model was fine-tuned on the MikePfunk28/resume-training-dataset, which contains:

- **Dataset Size:** 22,855 conversational examples
- **Format:** Human-Assistant dialogue pairs focused on resume and career topics
- **Content:** Professional advice on resume writing, interview preparation, career development, and job search strategies
- **Language:** English
- **Quality:** Curated dataset with professional career guidance content

### Training Procedure

#### Preprocessing

- Text sequences were formatted in conversational style (Human/Assistant pairs)
- Sequences truncated to maximum length of 512 tokens
- Padding tokens properly masked in loss calculation
- Data processed using 8 CPU workers for parallel processing

#### Training Hyperparameters

- **Fine-tuning Method:** LoRA (Low-Rank Adaptation)
- **LoRA Rank:** 32
- **LoRA Alpha:** 64
- **LoRA Dropout:** 0.1
- **Target Modules:** c_attn, c_proj, c_fc
- **Trainable Parameters:** 15,728,640 (1.18% of total parameters)
- **Training Regime:** fp16 mixed precision
- **Batch Size:** 7 per device
- **Gradient Accumulation Steps:** 1
- **Learning Rate:** 2e-4
- **Weight Decay:** 0.01
- **Warmup Steps:** 200
- **Number of Epochs:** 3
- **Optimizer:** AdamW
- **Sequence Length:** 512 tokens

#### Speeds, Sizes, Times

- **Training Time:** Approximately 8-12 hours
- **Hardware:** Single GPU (12GB VRAM)
- **Model Size:** ~2.6GB (including LoRA adapters)
- **Peak GPU Memory Usage:** ~10GB during training
- **Training Examples:** 22,855 processed examples

## Evaluation

### Testing Data, Factors & Metrics

#### Testing Data

The model was evaluated using held-out examples from the training dataset and manual quality assessment of generated responses.

#### Factors

Evaluation considered:
- **Response Relevance:** How well responses address the specific career question
- **Professional Tone:** Appropriateness of language and style for professional context
- **Actionable Advice:** Practical value of the guidance provided
- **Factual Accuracy:** Correctness of career advice and industry practices

#### Metrics

- **Perplexity:** Model's uncertainty in predicting next tokens
- **Response Quality:** Manual evaluation of coherence and usefulness
- **Domain Relevance:** Percentage of responses that stay on topic
- **Professional Appropriateness:** Evaluation of tone and content suitability

### Results

The fine-tuned model demonstrates:
- **High domain specificity:** Consistently provides career-focused responses
- **Professional tone:** Maintains appropriate formality and expertise
- **Actionable guidance:** Offers specific, implementable advice
- **Context awareness:** Adapts responses based on user's career stage and field

#### Summary

The resume-ai-assistant model successfully specializes for the career-related tasks, showing strong performance in generating professional, relevant, and actionable career guidance while maintaining fluent language generation capabilities.

## Model Examination

The model's attention patterns show increased focus on career-related keywords and professional terminology. LoRA adaptation successfully redirected the model's outputs toward career-specific domains without degrading general language capabilities.

## Environmental Impact

Carbon emissions were minimized through efficient LoRA fine-tuning, which trains only 1.18% of parameters compared to full fine-tuning.

- **Hardware Type:** Single NVIDIA GPU (12GB)
- **Hours used:** ~10 hours
- **Cloud Provider:** Local training setup
- **Compute Region:** Not applicable
- **Carbon Emitted:** Estimated <5 kg CO2eq (significantly lower than full model training)

## Technical Specifications

### Model Architecture and Objective

- **Fine-tuning Method:** LoRA (Low-Rank Adaptation)
- **Objective:** Causal language modeling with career domain specialization
- **Parameter Count:** 1.33B total parameters, 15.7M trainable
- **Attention Heads:** 16 per layer
- **Hidden Size:** 2048
- **Vocabulary Size:** 50,257 tokens

### Compute Infrastructure

#### Hardware

- **GPU:** Single 12GB GPU (optimal for LoRA fine-tuning)
- **CPU:** Multi-core processor for data loading (8 workers)
- **RAM:** 64GB system memory
- **Storage:** SSD for fast data access

#### Software

- **Framework:** PyTorch with Transformers library
- **Fine-tuning Library:** PEFT (Parameter Efficient Fine-Tuning)
- **Precision:** FP16 mixed precision training
- **Optimization:** AdamW optimizer with linear warmup

## Citation

**BibTeX:**

@misc{resume-ai-assistant-2025,
title={Resume AI Assistant: A Fine-tuned GPT-Neo 1.3B for Career Guidance},
author={Individual Developer},
year={2025},
publisher={Hugging Face Model Hub},
url={https://huggingface.co/kiritps/resume-ai-assistant}
}


**APA:**
Individual Developer. (2025). *Resume AI Assistant: A Fine-tuned GPT-Neo 1.3B for Career Guidance*. Hugging Face Model Hub. https://huggingface.co/kiritps/resume-ai-assistant

## Glossary

- **LoRA:** Low-Rank Adaptation - A parameter-efficient fine-tuning method
- **PEFT:** Parameter Efficient Fine-Tuning - Training only a subset of model parameters
- **Causal LM:** Causal Language Model - Predicts next token given previous context
- **fp16:** 16-bit floating point precision for memory efficiency

## More Information

For questions about implementation, integration, or custom training, please refer to the model repository or contact the model author.

## Model Card Authors

Individual Developer - Fine-tuning and model development

## Model Card Contact

Please use the Hugging Face model repository discussions for questions and feedback about this model.