Practical overview
When it fits
Use Minימind when you want to study, reproduce or adapt the training of a compact language model rather than rely only on a pre-trained model or documented hosted API.
Who it is for
- Learners studying how language models are trained
- Developers building small-model experiments
- Researchers seeking a compact baseline
- Educators creating a from-scratch LLM tutorial
Documented uses
- Train a small language model for educational experiments.
- Create a reproducible from-scratch LLM training tutorial.
- Use the 64M-parameter model as a compact research baseline.
- Adapt the training code for a domain-specific small language model.
Requirements & limits
No pricing or account requirements are documented.
- Whether your objective is model training rather than serving or API use
- Whether a 64M-parameter model is an appropriate scale for the experiment
- Whether Python is suitable for your development workflow
- Whether the Apache-2.0 license fits your intended use
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Train the MiniMind 64M language model from scratch
Use the repository’s documented training code and complete training pipeline to study or reproduce a small language model.
Before you start
- A cloned copy of the public MiniMind repository.
- Python and PyTorch, as the project’s core algorithms are implemented in native PyTorch.
- A single NVIDIA 3090 is documented for the SFT timing reference; the repository also documents single-machine single-GPU and multi-GPU training support.
- The project’s provided training data and code.
Steps
Obtain the MiniMind repository and its provided training data.
Follow the repository’s training pipeline, which covers pretraining, supervised fine-tuning, LoRA, RLHF-DPO, and RLAIF workflows.
Choose the documented single-GPU or multi-GPU training mode; multi-GPU options include DDP and DeepSpeed.
Train the approximately 64M-parameter model from scratch, then use the repository’s documented evaluation datasets or later training stages as appropriate.
Expected outcome
A trained small MiniMind language model produced through the repository’s documented from-scratch training workflow.
Guide limitations
- The documented two-hour figure refers specifically to one SFT epoch on a single NVIDIA 3090 and is not a general runtime guarantee.
- The evidence does not provide exact setup commands, dependency versions, dataset download commands, or hardware requirements beyond the stated examples.
- The evidence documents training and evaluation capabilities but does not establish that every listed workflow succeeds in a particular environment.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 30 September 2026
- Original website title
- jingyaogong/minimind · Official homepage title · retained in its source language