Text Generation
PEFT
Safetensors
English
pyspark
data-engineering
code-generation
qlora
lora
delta-lake
conversational
Instructions to use hoodarunner/pyspark-coding-assistant-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use hoodarunner/pyspark-coding-assistant-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3") model = PeftModel.from_pretrained(base_model, "hoodarunner/pyspark-coding-assistant-lora") - Notebooks
- Google Colab
- Kaggle
| from __future__ import annotations | |
| from pathlib import Path | |
| import pytest | |
| from spark_eval.runner import get_spark | |
| TASKS_DIR = Path(__file__).resolve().parent.parent / "tasks" | |
| def spark(): | |
| """One session for the whole test run. JVM startup dominates otherwise.""" | |
| session = get_spark("spark-eval-tests") | |
| session.sparkContext.setLogLevel("ERROR") | |
| yield session | |
| session.stop() | |