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LoNebula
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Model Details
Model Provider
LoNebula
Model Tree
Qwen/Qwen2.5-0.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
bash
pip install torch transformerspython
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer repo = "LoNebula/data-engineering-mentor"tokenizer = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo)messages = [ {"role": "system", "content": "You are a practical data engineering mentor. Explain trade-offs and validation steps."}, {"role": "user", "content": "How do I make an incremental model safe to rerun?"},]prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(prompt, return_tensors="pt").to(next(model.parameters()).device)with torch.inference_mode(): output = model.generate(**inputs, max_new_tokens=220, do_sample=False)print(tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))The model uses LoNebula/data-engineering-mentor-dataset, an original 50-example Q&A starter corpus written for this project. It does not copy or redistribute a third-party Q&A dataset. Answers were composed as concise explanations and link to relevant official documentation for further reading. The dataset is published under CC BY 4.0; retain attribution when redistributing the dataset or derived material.
Use it as a study companion for foundational data engineering concepts, interview practice, and first-pass design discussions. It is a small model trained on a small corpus, not a comprehensive or authoritative expert. It can omit context, make technical mistakes, and be outdated as product versions change. Verify operational advice against the linked official documentation and your organization's standards. Do not provide credentials, private data, or production secrets in prompts.
The held-out loss is a language-modeling measure on this small Q&A split; it is not a measure of engineering correctness. No broad benchmark or expert review is claimed.
Training examples link to official documentation for Apache Airflow, Apache Kafka, Apache Spark, dbt, Apache Iceberg, PostgreSQL, and OpenLineage. The explanations are original summaries, not copied documentation text.
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer repo = "LoNebula/data-engineering-mentor"tokenizer = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo)messages = [ {"role": "system", "content": "You are a practical data engineering mentor. Explain trade-offs and validation steps."}, {"role": "user", "content": "How do I make an incremental model safe to rerun?"},]prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(prompt, return_tensors="pt").to(next(model.parameters()).device)with torch.inference_mode(): output = model.generate(**inputs, max_new_tokens=220, do_sample=False)print(tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))