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Kus-hal
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Model Details
Model Provider
Kus-hal
Model Tree
Qwen/Qwen2.5-0.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Prompting a base instruct model for resume screening produces unstructured, inconsistent prose -- not something you can pipe into an ATS or scoring pipeline. This adapter makes structured JSON output the model's default behavior, not something you have to coax out with prompt engineering.
| Epoch | Training Loss | Validation Loss |
|---|---|---|
| 1 | 0.4078 | 0.2784 |
| 2 | 0.1956 | 0.1883 |
| 3 | 0.1713 | 0.1714 |
Validation loss tracked training loss closely with no divergence -- no overfitting.
Prompt: Screen this resume for a Backend Engineer position and return a structured verdict. Resume: Bachelor's in CS, 4 years distributed backend experience, skilled in Python, PostgreSQL, Docker, Kubernetes.
Base model (no fine-tuning): unstructured prose with markdown headers (Summary, Key Skills, Experience) -- not machine-parseable.
Fine-tuned model:
json
{"role": "Backend Engineer", "ats_score": 56, "verdict": "moderate_match", "matched_skills": ["Python", "PostgreSQL", "Docker", "Kubernetes"], "missing_skills": ["Java", "REST APIs", "Redis"], "years_experience": 4}
python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizerbase_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")model = PeftModel.from_pretrained(base_model, "Kus-hal/resume-screener-lora")tokenizer = AutoTokenizer.from_pretrained("Kus-hal/resume-screener-lora")
PEFT/LoRA, Hugging Face Transformers, TRL, PyTorch, Colab T4 GPU