Coding

MiniMind

Train small language models from scratch with complete PyTorch pipeline in just 2 hours

Opens jingyaogong.github.io in a new tab.

How the 7.6 was reached

  1. Quality of output8/10

    62,855 GitHub stars, active, Apache-2.0; heavy adoption for a small-model training project, though the 64M model is naturally limited. Source: github.com

  2. Ease of use6/10

    Pure PyTorch code covering the full pipeline, but you need a GPU and training know-how; docs found are limited. Source: jingyaogong.github.io

  3. Pricing value9/10

    Free Apache-2.0 code; training a 64M model costs about $3 on a single 3090. Source: github.com

  4. API and integration quality7/10

    Works with vLLM, ollama, llama.cpp, SGLang, transformers, and offers an OpenAI-compatible API for Open-WebUI and Dify. Source: jingyaogong.github.io

  5. Solves the problem it claims to solve8/10

    Trains a small chatbot from scratch cheaply; 62,855 stars show wide adoption, though cost and time figures come from the project. Source: github.com

7.6/10 is the average of the five criterion scores above. The evidence comes from 3 different sources.Scores come from public evidence (the tool's own pages and documentation, and independent reviews); we do not test every tool hands-on. Scored 29 September 2026, on version 1 of our five-criteria scoring method.

Pricing

Open Source
Free
  • Complete source code
  • Training pipeline
  • Multiple model architectures
  • Documentation

For the full list of what each plan includes, and current prices, see github.com.

Key Features

  • 64M parameter ChatBot training
  • Complete pipeline from tokenizer to deployment
  • RLAIF algorithms (PPO, GRPO, CISPO)
  • Compatible with vLLM, ollama, llama.cpp
  • OpenAI API drop-in replacement

Pros & Cons

Pros

  • Ultra-low cost training (~¥3)
  • Pure PyTorch implementation with no black boxes
  • Complete end-to-end pipeline
  • Multiple deployment options
  • Active development with latest RL techniques

Cons

  • Requires technical expertise in ML
  • Limited to small model sizes
  • Documentation primarily in Chinese
  • Requires GPU hardware for training
Verdict

MiniMind is an excellent educational tool for learning LLM training from scratch. While limited to smaller models, it provides a complete, transparent pipeline that's perfect for understanding the fundamentals of language model development.

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