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- Build a Machine Learning Model Using Just Natural Language
Build a Machine Learning Model Using Just Natural Language
.. with just a prompt
In today’s newsletter:
Build Machine Learning Models just using Natural Language
Search and get the LLM Ready Data for AI Agents
100+ Fine-tuning notebooks for LLaMA, Qwen, DeepSeek, Gemma, and Phi, all in one place.
Reading time: 3 minutes.
Build Machine Learning models just using natural language!
Plexe AI lets you create machine learning models by describing them in plain language.
Simply explain what you want, and the AI-powered system builds a fully functional model through an automated agentic approach.
Here is why it's a game changer:
Natural Language Models: Define models in plain English.
AI Agent Team: Automates planning, coding, testing, and packaging.
One-Call Model Build: Create models with a single method.
Ray Support: Fast, distributed training and evaluation.
Smart Data Handling: Auto schema inference and data generation.
👉 It’s 100% open source. You can find the repo here → Plexe AI
Search the web and get LLM ready data with just a few lines of Python code!
Firecrawl released the new search endpoint that lets you perform web searches, and scrape their full content in a single operation.
Key Features:
Choose specific output formats (markdown, HTML, links, screenshots)
Search the web with customizable parameters (language, country, etc.)
Optionally retrieve content from search results in various formats
Control the number of results and set timeouts
Check out the below code to get started.
Unsloth AI released this repository containing 100+ fine-tuning notebooks for LLaMA, Qwen, DeepSeek, Gemma, and Phi, all in one place.
Includes complete guides & examples for:
Use cases: Tool-calling, Classification, Synthetic data & more
End-to-end workflow: Data prep, training, running & saving models
BERT, TTS Vision models & more
Training methods like: GRPO, DPO, Continued Pretraining, SFT, Text Completion & more!
Llama, Qwen, DeepSeek, Gemma, Phi & more
It’s 100% Open Source
That’s a Wrap
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