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cyb3rr31a/
tinyllama-data-engineering

Name: tinyllama-data-engineering
Availability: InStock

TinyLlama/TinyLlama-1.1B-Chat-v1.0

finetune

apache-2.0

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TinyLlama Data Engineering Assistant

A TinyLlama-1.1B model fine-tuned on data engineering Q&A pairs using QLoRA. It answers questions about data engineering concepts more accurately than the base model.

Base model

TinyLlama/TinyLlama-1.1B-Chat-v1.0

Training

Method: QLoRA (4-bit quantization + LoRA)
Dataset: 15 custom data engineering Q&A pairs
Epochs: 10
LoRA rank: 16
Hardware: NVIDIA T4 (Google Colab free tier)

Topics covered

ETL, data warehouses, data lakes, Apache Spark, dbt, Apache Airflow, DAGs, batch vs stream processing, data pipelines, partitioning, data lineage, medallion architecture, idempotency, BigQuery, dimensional modeling, RAG

How to use

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "cyb3rr31a/tinyllama-data-engineering"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16)

prompt = "### Question:\nWhat is dbt?\n\n### Answer:\n"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Limitations

This model was fine-tuned on a small dataset of 15 examples for demonstration purposes. It performs best on the topics covered in the training data.