---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen3-0.6B
tags:
- job-parsing
- qwen3
- lora
- json-extraction
---

# 📦 Qwen3-0.6B — Job Description Struct-Extractor

A fine-tuned version of **Qwen3-0.6B** designed for accurate extraction of structured job attributes from raw job descriptions.  
This model outputs strict, schema-aligned JSON, making it perfect for downstream applications like search, analytics, and recommendation systems.

---

## 🚀 Model Highlights

- **Base Model:** Qwen/Qwen3-0.6B  
- **Architecture:** Decoder-only Transformer (Causal LM)  
- **Tokenizer:** QwenTokenizer (same as base)  

**Fine-Tuned For:**
- Zero-hallucination extraction  
- Schema-conformant JSON outputs  

---

## 🎯 Task Overview

- **Task:** Extract structured fields from job descriptions  
- **Output:** JSON strictly following a predefined schema  

**Use Cases:**
- Automated JD parsing into structured fields  
- Talent platform search & recommendation engines  
- HR data cleaning & analytics pipelines  
- Resume ↔ Job matching systems  

---

## 🖥️ Inference Example (Python)

```python
import torch
import re
import time
import json
import json5
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

# Model paths
base_model_id = "Qwen/Qwen3-0.6B"
lora_model_id = "Rithankoushik/Qwen-0.6-Job-parser-Model"

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token

# Load model + LoRA
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    trust_remote_code=True,
    torch_dtype=torch.float16,
    device_map="auto"
)
model = PeftModel.from_pretrained(base_model, lora_model_id, device_map="auto")
model = model.merge_and_unload()
model.eval()

def extract_and_clean_json(text):
    """Extract JSON from LLM output, even if extra text is present."""
    match = re.search(r"\{[\s\S]*\}", text)
    if not match:
        return None

    json_str = match.group(0)
    json_str = json_str.replace("None", "null")
    json_str = json_str.replace("True", "true").replace("False", "false")
    json_str = re.sub(r",(\s*[}\]])", r"\1", json_str)

    try:
        return json5.loads(json_str)
    except Exception as e:
        print(f"JSON parse error: {e}")
        return None


def infer_from_text(jd_text: str):
    """Runs inference on a job description."""
    start_time = time.time()

    system_prompt = """Extract structured information from the following job description and return it as JSON.
""".strip()

    user_prompt = f"""
Job Description:
{jd_text}
""".strip()

    messages = [
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": user_prompt}
    ]

    prompt = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True
    )

    raw_inputs = tokenizer(prompt, return_tensors="pt")
    device = model.device
    inputs = {k: v.to(device) for k, v in raw_inputs.items()}

    with torch.no_grad():
        out = model.generate(
            **inputs,
            max_new_tokens=1000,
            do_sample=False,
            temperature=0,
            pad_token_id=tokenizer.eos_token_id
        )

    gen_tokens = out[0][inputs["input_ids"].shape[1]:]
    response_text = tokenizer.decode(gen_tokens, skip_special_tokens=True)
    duration = round(time.time() - start_time, 2)

    parsed = extract_and_clean_json(response_text)
    if parsed is not None:
        return json.dumps(parsed, indent=2), duration

    return response_text, duration