posttrainllm.com valid
PostTrainLLM
A Mac-local LLM specialist factory: post-training, evidence-gated packaging, MLX runtime work, and a WebGPU learning playground.
- Authority
- 46 reach 0 · trust 6 · quality 25 · freshness 15
- Tier
- listed
- llms.txt
- https://posttrainllm.com/llms.txt
- llms-full.txt
- https://posttrainllm.com/llms-full.txt
- Sections
- 6
- Links
- 78
- Size
- 16333 bytes
- Registered
- 2026-07-04
- Country
- — Publisher location unknown; CDN address excluded
- Country evidence
- No published address evidence collected yet
- For agents
- Documentation: developer or product docs worth reading, nothing to call
- First seen
- 2026-10-27T13:42:15.560Z
- Last checked
- 2026-10-27T13:42:17.835Z
- Badge
Their llms.txt
The beginning of the file, from our validated copy saved 2026-10-27T13:42:15.567Z. Cached copy · Source on posttrainllm.com
# PostTrainLLM
> A Mac-local LLM specialist factory: post-training, evidence-gated packaging, MLX runtime work, and a WebGPU learning 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): 128 resolved attempts — 5 worked, 55 worked with caveat, 68 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
- [Studied projects](https://posttrainllm.com/studies): 20 source-linked evidence dossiers
- [Inspiration](https://posttrainllm.com/inspiration): credited tools and projects, local lessons, and source trails
- [Documentation](https://posttrainllm.com/docs/): 324 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 0.2.0 (2)
- [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): Bounded full public page content
## Boundaries
Public machine resources are catalogued separately from HTML pages. Local
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