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
File size: 433 Bytes
de46078 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | 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"
@pytest.fixture(scope="session")
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()
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