Base Model: Qwen (qwen/Qwen3-0.6B)
Fine-tuned on: 80+ custom-labeled job descriptions
Trained using: Hugging Face Transformers & TRL's SFTTrainer
Dataset Format: Few-shot prompting with Qwen’s <|im_start|> / <|im_end|> chat template
Output: Structured JSON response

🚀 How to Use

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Rithankoushik/job-parser-model-qwen"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16)

prompt = """<|im_start|>system
You are a helpful assistant that extracts structured information from job descriptions.
<|im_end|>
<|im_start|>user
[Paste job description here]
<|im_end|>
<|im_start|>assistant
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=1024)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))