# PostTrainLLM

> A Mac-local LLM specialist factory: post-training, evidence-gated packaging,
> MLX runtime work, and a WebGPU playground.

## When to use this

Reach for PostTrainLLM when you need to train, fine-tune, evaluate, or package a specialist LLM on a single Apple Silicon Mac — without cloud compute. Best-fit jobs:

- Fine-tuning an open model with LoRA/QLoRA on a Mac (MLX)
- Evaluating a local model against frozen benchmarks (BFCL, tool-calling, perplexity)
- Packaging a trained specialist for MLX or on-device inference
- Comparing Mac-local training approaches (distillation, GRPO, SFT)
- Training a tiny model in-browser and inspecting its loss and samples
- Running client-side inference and validating WebGPU kernels

Do not use PostTrainLLM for: large-scale distributed training, frontier-scale pretraining, or anything that requires a GPU cluster — it is designed for one Mac.

## Public surface

- [Home](https://posttrainllm.com/): Product and research-lab overview
- [Native factory quickstart](https://posttrainllm.com/docs/quickstart): Target → data → post-train → eval → package → report
- [Browser training lab](https://posttrainllm.com/playground): TinyGPT training with visible loss and samples
- [Browser inference lab](https://posttrainllm.com/inference): Client-side model inference
- [WebGPU kernel tests](https://posttrainllm.com/webgpu-test): Browser kernel correctness and capability probes
- [Training dashboard](https://posttrainllm.com/training-dashboard): Inspect recorded training state
- [Experiments](https://posttrainllm.com/experiments): 76 resolved attempts — 5 worked, 37 worked with caveat, 34 non-positive or mixed
- [Recipes](https://posttrainllm.com/recipes): 18 reproducible or explicitly bounded recipe contracts
- [Learning paths](https://posttrainllm.com/learn): 9 paths and 13 buildable artifacts
- [Documentation](https://posttrainllm.com/docs/): 310 source documents
- [Artifacts](https://posttrainllm.com/artifacts): Public packages, evidence, and blockers
- [Benchmarks](https://posttrainllm.com/benchmarks): Measured performance evidence
- [Evaluation leaderboard](https://posttrainllm.com/leaderboard): Model-quality comparisons and gates
- [Mac app](https://posttrainllm.com/download): Verified release 0.1.0 (1)
- [Devlog](https://posttrainllm.com/devlog): Build history
- [Agent catalog](https://posttrainllm.com/api/ai): Complete page-to-Markdown inventory
- [OpenAPI spec](https://posttrainllm.com/openapi.json): Machine-readable API description
- [Sitemap](https://posttrainllm.com/sitemap.xml): Canonical public HTML routes
- [Full agent index](https://posttrainllm.com/llms-full.txt): Every public page grouped by kind

## Boundaries

Public machine resources are catalogued separately from HTML pages. Local
factory runs, models, private artifacts, and unpublished evidence are excluded.
`decision.json` remains the terminal quality and product authority.

## CLI

PostTrainLLM ships a native Mac CLI for the full factory loop:

```bash
# build the native factory CLI
git clone https://github.com/PostTrainLLM/posttrainllm && cd native-mac
swift build --product posttrainllm

# distill a specialist, gate it, serve it
posttrainllm distill --teacher qwen3 --student …
posttrainllm eval-gate --spec sql.json --candidate …
posttrainllm serve --port 8080  # OpenAI-compatible
```

The CLI exposes 100+ subcommands covering train, eval, package, serve, and
inspect. See [the generated CLI reference](https://posttrainllm.com/docs/cli-reference) or
`posttrainllm --help` for the full command surface.