Instructions to use aditismile/resume_enhnaced with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aditismile/resume_enhnaced with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="aditismile/resume_enhnaced")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("aditismile/resume_enhnaced", dtype="auto")

PEFT
How to use aditismile/resume_enhnaced with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use aditismile/resume_enhnaced with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aditismile/resume_enhnaced"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aditismile/resume_enhnaced",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aditismile/resume_enhnaced

SGLang

How to use aditismile/resume_enhnaced with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "aditismile/resume_enhnaced" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aditismile/resume_enhnaced",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "aditismile/resume_enhnaced" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aditismile/resume_enhnaced",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use aditismile/resume_enhnaced with Docker Model Runner:
```
docker model run hf.co/aditismile/resume_enhnaced
```

phi3-full-resume-enhancer

This is a fine-tuned version of microsoft/Phi-3-mini-4k-instruct for resume enhancement and professional writing.

Model Description

This model transforms unstructured, informal resumes into professional, well-formatted resumes with:

Quantified achievements
Action-oriented language
Professional formatting
Enhanced skill descriptions
Structured sections

Training Data

The model was fine-tuned on 5 high-quality examples demonstrating transformation from casual/unstructured resumes to professional formats across various roles (Software Developer, Marketing Professional, Data Analyst, Project Manager, Software Engineer).

How to Use

Installation

pip install transformers peft torch

Basic Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch

# Load model and tokenizer
base_model_id = "microsoft/Phi-3-mini-4k-instruct"
adapter_model_id = "aditismile/resume_enhnaced"

tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True)
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    device_map="auto",
    torch_dtype=torch.float16,
    trust_remote_code=True
)

model = PeftModel.from_pretrained(base_model, adapter_model_id)
model.eval()

# Prepare input
resume_text = """John Doe
john@email.com

Summary: Developer with some experience

Work:
- Coded stuff at Company XYZ
- Fixed bugs

Skills: Python, JavaScript"""

prompt = f"""<|system|>
You are an expert resume writer. Transform the following resume into a professional format.<|end|>
<|user|>
{resume_text}<|end|>
<|assistant|>
"""

# Generate enhanced resume
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=800,
    temperature=0.7,
    top_p=0.9,
    do_sample=True
)

result = tokenizer.decode(outputs[0], skip_special_tokens=True)
enhanced_resume = result.split("<|assistant|>")[-1].strip()
print(enhanced_resume)

Training Details

Base Model: microsoft/Phi-3-mini-4k-instruct
Fine-tuning Method: LoRA (Low-Rank Adaptation)
Training Examples: 5 full resume transformations
Max Length: 2048 tokens
Epochs: 5
Learning Rate: 2e-4
LoRA Rank: 16
LoRA Alpha: 32

Limitations

Trained on a small dataset (5 examples) - may benefit from additional training data
Best suited for technical and professional roles
May hallucinate quantifiable metrics if not present in original resume
Designed for English resumes only

Intended Use

This model is intended for:

Resume enhancement and professional writing assistance
Career coaching tools
Job application preparation
Educational purposes in resume writing

Citation

If you use this model, please cite:

@misc{phi3_full_resume_enhancer,
  author = {aditismile},
  title = {{Phi-3 Resume Enhancement Model}},
  year = {{2024}},
  publisher = {{Hugging Face}},
  howpublished = {{\url{{https://huggingface.co/aditismile/resume_enhnaced}}}}
}

License

This model inherits the license from microsoft/Phi-3-mini-4k-instruct. The fine-tuned adapters are released under MIT license.

Contact

For questions or feedback, please open an issue on the model repository.

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Base model

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