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What it contains
Minimal end-to-end LLM training harness covering tokenizer, pretraining, SFT, RL, evaluation, inference, and scaling/speedrun scripts.
Typical uses
Train/study small-to-GPT-2-scale models with a compact PyTorch codebase and reproducible run recipes.
Why people choose it
Inference: chosen for end-to-end comprehensibility rather than a large configurable training framework.
Last meaningful updateUnknown
Production readinessdevelopment
Classification
Subject domains
LLM trainingML research
Task categories
LLM trainingML research
Project phases
researchimplementation
Secondary types
training harnessCLI
Select when
- Need understandable end-to-end LLM training/research
Do not select when
- Need production distributed-training platform
- Inference-only need
Implementation complexitymedium
Routing heuristic 78.0%
Security boundary clarity
3/5 First-party routing documentation
5/5 Strengths
None documented.
Poor-fit scenarios
- Need production distributed-training platform
- Inference-only need
Limitations
None documented.
nanochat/
GPT/optimizer/data/tokenizer/eval/inference.
training core
scripts/
Train/eval/SFT/RL/chat/tokenizer/benchmarks.
trainingeval
runs/
Speedrun/scaling/miniseries/CPU scripts.
experiments
tasks/
Evaluation task adapters.
eval tasks
Operating systemsNone documented
Installation methodsNone documented
InterfacesNone documented
Required credentialsNone documented
External servicesNone documented
HardwareCPU/MPS for small demos, GPU/8xH100 reference workflows
Major dependenciesNone documented
Compatibility notes
None documented.
One-sentence semantic summary
Minimal end-to-end LLM training harness covering tokenizer, pretraining, SFT, RL, evaluation, inference, and scaling/speedrun scripts.
Capability keywords
tokenizationpretrainingSFTRLevaluationinferencescaling
User intent phrases
- Need understandable end-to-end LLM training/research
Negative match phrases
- Need production distributed-training platform
- Inference-only need
Differentiators
None documented.
Security considerations
None documented.
Privacy considerations
None documented.
Uncertainty
Unresolved items
None documented.
Inference notes
None documented.
Repository usage guidance
installation summaryUnknown
basic usage summaryTrain/study small-to-GPT-2-scale models with a compact PyTorch codebase and reproducible run recipes.
documented entry pointsruns/speedrun.sh, python -m scripts.chat_cli
key configuration filesNone documented
important directoriesnanochat/, scripts/, runs/, tasks/
documentation pathsREADME.md
example pathsNone documented
Relationships
downstream
high
Autoresearch uses a simplified nanochat-derived training setup.
Recommended combinations
LLM research loop high
Each repository covers a distinct layer; avoid combining tools that duplicate lifecycle ownership.
Order of operations
- Understand/reproduce nanochat baseline
- Set up autoresearch on suitable GPU
- Define program.md policy
- Run bounded experiments and review logs
Routing rules
rule_041: Need understandable end-to-end LLM training/research P959
Rationale
Minimal end-to-end LLM training harness covering tokenizer, pretraining, SFT, RL, evaluation, inference, and scaling/speedrun scripts.
Required conditions
None documented.
Preferred
karpathy/nanochat
Fallback
None