HelixCipher/job

HelixCipher/job-posting-extractor-qwen · Hugging Face Job Posting Extractor (Qwen2.5-3B) A fine-tuned version of Qwen2.5-3B-Instruct specialized in extracting structured JSON data from job postings. Built to replace expensive API calls for web scraping tasks. What This Model Does Given a job posting in markdown format, this model extracts structured JSON containing: job_title company location description salary (when available) requirements (when available) Quick Start ``` from unsloth import FastLanguageModel import json Load model from HuggingFace model, tokenizer = FastLanguageModel.from_pretrained( model_name="HelixCipher/job-posting-extractor-qwen", max_seq_length=2048, load_in_4bit=True, ) FastLanguageModel.for_inference(model) Example input job_markdown = """ Job Position Position: Senior Python Developer Company: TechCorp Location: San Francisco, CA Job Description We are looking for an experienced Python developer... """ Extract JSON messages = {"role": "system", "content": "You are a JSON extraction assistant. Always output ONLY valid JSON."}, {"role": "user", "content": f"Extract job fields as JSON.\n\n{job_markdown}"} prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = tokenizer(prompt, return_tensors="pt").to("cuda") outputs = model.generate(inputs, max_new_tokens=500, temperature=0.1) result = tokenizer.decode(outputs0, skip_special_tokens=True) print(result) ``` Training Details Base Model: unsloth/qwen2.5-3b-instruct-bnb-4bit. Training Data: 12,000 job posting examples. Training Approach: LoRA with Unsloth. Fine-tuning Library: TRL (Transformer Reinforcement Learning). Framework Versions PEFT: 0.18.1 TRL: 0.24.0 Transformers: 4.57.6 PyTorch: 2.10.0+cu126 Use Cases Extract job postings from scraped websites. Convert unstructured job listings to structured JSON. Automate data collection for job aggreg