MiniMind documentation; discovered through the weekly chart · First found 2026-09-06
MiniMind: learn how a small language model is trained
MiniMind uses a small model and tutorial code to explain the roles of pretraining and supervised fine-tuning.
Learn pretraining and supervised fine-tuning
MiniMind uses a roughly 64M-parameter model and tutorial code to expose training stages, including pretraining and supervised fine-tuning. Pretraining builds a base; fine-tuning teaches more explicit task behavior. The code offers a way to study each stage.
Which stage took “two hours”?
The maintainer’s “two hours” refers to one supervised-fine-tuning epoch on a single RTX 3090, not the entire training process. Reproduction time also depends on data, batch size, code revision and hardware.
Important additions and corrections
Dates below mark changes to our coverage.
- Addition ·
Clarified the training conditions: “two hours” means one supervised fine-tuning epoch on an RTX 3090, not the full training process.
Sources and notes
sources中文
Sources and notes
- Read the originalMiniMind documentation; discovered through the weekly chart
- MiniMind README at a pinned revision
Retained summary and context
For learning the training process: start with examples and hardware requirements.
The timing is a maintainer report under specific conditions, not the full training workflow. We have not reproduced it; Star counts do not establish results or production suitability.
Sources and evidence · 2 links
Each link has a purpose and scope. Link counts do not establish independent confirmation; direct support applies only to the statements specified below.
Teaching scope and timing conditions.
Limits: Maintainer documentation; no independent training run.
Source timeline
- · GitHub weekly Trending ranked #5, recording 3,649 stars for the week.
Collection record
First discovered:
Collection and source publication have distinct dates.
Editorial record
Sources reviewed
· Beijing time (UTC+8)
Read source materials to clarify purpose and limits. Discovery and event dates are retained; editorial review is not an independent experiment.
Sources and full record
This dates our review of the wording and sources, not a project release or product update.
Full record: before, after and sources (JSON)