Best alternatives to Build a Reasoning Model (From Scratch)

Build a Large Language Model (From Scratch)

#1

Learn to build an LLM from the ground up with hands-on code

This Manning Publications book by Sebastian Raschka guides readers through building a GPT-style large language model from scratch using Python and PyTorch. It covers tokenization, transformer architecture, pre-training, and fine-tuning with clear, step-by-step code examples. The book is designed for ML practitioners and engineers who want a deep, practical understanding of how modern LLMs like ChatGPT actually work under the hood.

Kiln

#2

AI fine-tuning and dataset curation platform for custom models

Kiln is a platform for fine-tuning large language models and managing training datasets with a collaborative, visual interface. Teams can curate examples, run fine-tuning jobs, and compare model versions to improve task-specific performance. Built for ML teams and product engineers who want to customize models without deep infrastructure expertise.

TensorZero

#3

LLM gateway for production optimization, routing, and fine-tuning

TensorZero is an open-source LLM gateway that collects production inference data and uses it to continuously optimize prompts, route to better models, and generate fine-tuning datasets. It wraps existing LLM APIs and learns from real user feedback and outcomes to improve quality over time. Built for ML teams who want to systematically improve production AI systems.

Together AI

#4

Run and fine-tune leading open-source LLMs at cloud scale

Together AI is a cloud platform for deploying, fine-tuning, and serving open-source foundation models such as Llama, Mistral, and FLUX at high throughput. It offers a unified API compatible with OpenAI's interface and dedicated GPU instances. Ideal for AI teams building production applications who need cost-efficient, scalable inference.

Unsloth

#5

Fine-tune LLMs up to 5x faster with 70% less VRAM usage

Unsloth is an open-source library that dramatically accelerates LLM fine-tuning by rewriting key CUDA kernels in Triton. It supports QLoRA and LoRA training for Llama, Mistral, Gemma, and other models with minimal code changes. Targeted at ML engineers and researchers who want to fine-tune large models on consumer or budget GPUs.

Vanna.ai

#6

Train an AI model on your schema and query data in plain English

Vanna.ai is an open-source Python framework that lets you fine-tune a retrieval-augmented LLM on your own database schema and query history. It translates natural language questions into accurate SQL and can run in a notebook or Slack bot. Designed for data teams who need a customizable, private text-to-SQL solution.

AI/ML API

#7

Unified API marketplace for 200+ AI and machine learning models

AI/ML API is a single API platform that aggregates access to over 200 AI models including LLMs, image generators, speech, and embeddings from providers like OpenAI, Anthropic, and Stability AI. It offers competitive pricing, pay-per-use billing, and OpenAI-compatible endpoints. Ideal for developers who want one integration point for multiple AI capabilities.

ChatGPT prompt engineering for developers

#8

DeepLearning.AI short course on prompt engineering with GPT

A free short course by DeepLearning.AI and OpenAI, taught by Isa Fulford and Andrew Ng, covering best practices for prompting ChatGPT in application development. Topics include iterative prompt refinement, summarization, inference, transformation, and building chatbots. Ideal for developers integrating LLMs into software products.

Ghostwriter

#9

Replit's AI coding assistant for in-browser development and debugging

Ghostwriter is Replit's built-in AI coding assistant that provides code completion, explanation, debugging, and generation directly inside the Replit IDE. It understands the full project context and can refactor code or answer questions about the codebase in real time. Ideal for students, hobbyists, and developers building and learning directly in Replit's cloud environment.