Instructions to use Atrayee29/resume-jd-analyzer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Atrayee29/resume-jd-analyzer with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B-Instruct")
model = PeftModel.from_pretrained(base_model, "Atrayee29/resume-jd-analyzer")

Transformers

How to use Atrayee29/resume-jd-analyzer with Transformers:

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

pipe = pipeline("text-generation", model="Atrayee29/resume-jd-analyzer")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Atrayee29/resume-jd-analyzer", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Atrayee29/resume-jd-analyzer with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Atrayee29/resume-jd-analyzer"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Atrayee29/resume-jd-analyzer",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Atrayee29/resume-jd-analyzer

SGLang

How to use Atrayee29/resume-jd-analyzer 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 "Atrayee29/resume-jd-analyzer" \
    --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": "Atrayee29/resume-jd-analyzer",
		"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 "Atrayee29/resume-jd-analyzer" \
        --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": "Atrayee29/resume-jd-analyzer",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Atrayee29/resume-jd-analyzer with Docker Model Runner:
```
docker model run hf.co/Atrayee29/resume-jd-analyzer
```

Resume–JD Match Analyzer

QLoRA Adapter for Llama-3.2-3B-Instruct

Overview

This model is a QLoRA fine-tuned adapter built on top of meta-llama/Llama-3.2-3B-Instruct.

The model analyzes the match between a candidate resume and a job description and returns structured JSON containing:

match_score
missing_skills
resume_improvements
interview_focus_areas

Training Details

Base Model

meta-llama/Llama-3.2-3B-Instruct

Fine-Tuning Method

QLoRA
PEFT
TRL SFTTrainer
4-bit NF4 quantization

Dataset

Synthetic dataset generated using GPT-4o.

Dataset size:

500 total examples
400 training examples
100 validation examples

Roles included:

Backend Engineer
Frontend Engineer
Full Stack Engineer
Data Scientist
ML Engineer
DevOps Engineer
Cloud Engineer
Cybersecurity Analyst
AI Engineer

Training Configuration

Epochs: 2
LoRA Rank (r): 16
LoRA Alpha: 32
LoRA Dropout: 0.05
Optimizer: paged_adamw_8bit
Max Sequence Length: 1250

Validation Metrics

Validation Loss: 0.7125
Mean Token Accuracy: 79.3%

Example Output

{
  "match_score": 82,
  "missing_skills": [
    "Kubernetes"
  ],
  "resume_improvements": [
    "Add Kubernetes project experience"
  ],
  "interview_focus_areas": [
    "Distributed systems",
    "Container orchestration"
  ]
}

Intended Use

Resume screening, candidate evaluation, recruiting workflows, and interview preparation assistance.

Limitations

Trained on synthetic data.
Match scores should be treated as recommendations rather than hiring decisions.
Human review is recommended for production use.

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