Never train another ML model again

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Never train another ML model again

Hello, Hacker News community! I made FlashLearn, an open-source library designed to streamline the integration of Large Language Models (LLMs) into your workflows. With FlashLearn, you can effortlessly build JSON-based pipelines for tasks like classification and labeling using just a few lines of code, while maintaining standardized outputs for seamless downstream processing. Key Features: * Quick Setup : Install FlashLearn via PyPI: ``` pip install flashlearn ``` Additionally, set your LLM provider credentials if you're using OpenAI / Deepseek: ``` export OPENAI_API_KEY="YOUR_API_KEY" ``` * JSON-Centric Pipelines : Easily structure and process data. Here's an example of performing sentiment analysis on IMDB movie reviews using a prebuilt skill: ```python from flashlearn.utils import imdb_reviews_50k from flashlearn.skills import GeneralSkill from flashlearn.skills.toolkit import ClassifyReviewSentiment # Load data and skills data = imdb_reviews_50k(sample=100) skill = GeneralSkill.load_skill(ClassifyReviewSentiment) tasks = skill.create_tasks(data) # Process tasks in parallel results = skill.run_tasks_in_parallel(tasks) # Save outputs as clean JSON import json with open('sentiment_results.jsonl', 'w') as f: for task_id, output in results.items(): input_json = data[int(task_id)] input_json['result'] = output f.write(json.dumps(input_json) + '\n') ``` * Multi-Step Pipelines : Chain and extend workflows by passing structured outputs to subsequent skills. ```python # Example of chaining tasks # next_skill = ... # next_tasks = next_skill.create_tasks([...based on 'output'...]) # next_results = next_skill.run_tasks_in_parallel(next_tasks) ``` * Custom Skills : For domain-specific needs, easily define custom skills: ```python from flashlearn.skills.learn_skill import LearnSkill learner = LearnSkill(model_name="gpt-4o-mini") skill = learner.learn_skill(data, task='Define categories "satirical", "quirky", "absurd".') tasks = skill.create_tasks(data) ``` * Image Classification : Handle visual data with ease using flexible tools for single and multi-label classification. ```python from flashlearn.skills.classification import ClassificationSkill images = [...] # Base64-encoded images skill = ClassificationSkill( model_name="gpt-4o-mini", categories=["cat", "dog"], max_labels=1, system_prompt="Classify images." ) tasks = skill.create_tasks(images, column_modalities={"image_base64": "image_base64"}) results = skill.run_tasks_in_parallel(tasks) ```