# 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): 75 resolved attempts — 4 worked, 38 worked with caveat, 33 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/): 309 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): Release 0.1.0 (1) — notarization pending, not yet downloadable - [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. ## Application and research pages - [PostTrainLLM — Build and measure specialist LLMs on a Mac](https://posttrainllm.com/) — [Markdown](https://posttrainllm.com/index.md) — Build bounded specialist language models sized to one Apple Silicon machine, then distill, fine-tune, evaluate, package, and inspect them with reproducible… - [How to build an agent trajectory dataset | PTLLM](https://posttrainllm.com/agent-trajectory-dataset) — [Markdown](https://posttrainllm.com/agent-trajectory-dataset.md) — Build an agent trajectory dataset with typed events, provenance, privacy filters, leakage-safe splits, and outcome-based evaluation. - [posttrainllm — public artifacts](https://posttrainllm.com/artifacts) — [Markdown](https://posttrainllm.com/artifacts.md) — Public posttrainllm artifacts: measured model packages, routed specialists, benchmark reports, blockers, and release status. - [Artifact: ANE M8 Core ML Chain | PTLLM](https://posttrainllm.com/artifacts/ane-m8-coreml-chain) — [Markdown](https://posttrainllm.com/artifacts/ane-m8-coreml-chain.md) — A layer-chunked Core ML chain ran a Qwen3 28-block path on the Apple Neural Engine at about 17 tok/s. - [Artifact: Browser WebGPU Training Speedup | PTLLM](https://posttrainllm.com/artifacts/browser-webgpu-speedup) — [Markdown](https://posttrainllm.com/artifacts/browser-webgpu-speedup.md) — The original browser posttrainllm track: hand-written WebGPU kernels beat WASM SIMD more as model width grows. - [Artifact: Factory Run Schema v1 | PTLLM](https://posttrainllm.com/artifacts/factory-run-schema-v1) — [Markdown](https://posttrainllm.com/artifacts/factory-run-schema-v1.md) — The canonical target, data, post-training, evaluation, package, and report shape for PostTrainLLM runs. - [Artifact: 4-bit Browser Gallery Models | PTLLM](https://posttrainllm.com/artifacts/gallery-int4-browser-models) — [Markdown](https://posttrainllm.com/artifacts/gallery-int4-browser-models.md) — The browser gallery ships fp16 and int4 variants so model downloads are smaller and cold-start is cheaper. - [Artifact: Hugging Face Specialist Model Archive v1 | PTLLM](https://posttrainllm.com/artifacts/hf-specialist-model-archive-v1) — [Markdown](https://posttrainllm.com/artifacts/hf-specialist-model-archive-v1.md) — The local specialist model cache was promoted to Hugging Face or deleted when safely re-downloadable from upstream repos. - [Artifact: Huge Preset Decode Throughput | PTLLM](https://posttrainllm.com/artifacts/huge-decode-throughput) — [Markdown](https://posttrainllm.com/artifacts/huge-decode-throughput.md) — The native Mac runtime reached high local decode throughput on the Huge preset, showing the serving path is viable for local eval loops. - [Artifact: Memory64 Browser Behemoth Allocation | PTLLM](https://posttrainllm.com/artifacts/memory64-browser-behemoth) — [Markdown](https://posttrainllm.com/artifacts/memory64-browser-behemoth.md) — A WebAssembly Memory64 build lifted the browser model allocation ceiling past the old 4GB tab limit. - [Artifact: Needle 2 Tool-Selection Boundary | PTLLM](https://posttrainllm.com/artifacts/needle2-tool-selection) — [Markdown](https://posttrainllm.com/artifacts/needle2-tool-selection.md) — Needle 2 is tiny and fast, but neither the full catalog nor oracle-selected task catalogs made it accurate or safe enough for Pace. - [Artifact: OffHours Context-Interference Benchmark | PTLLM](https://posttrainllm.com/artifacts/offhours-context-interference) — [Markdown](https://posttrainllm.com/artifacts/offhours-context-interference.md) — A paired office-work benchmark that separates unresolved personal context from interruption and raw context volume. Devin showed no mental-toll penalty, but… - [Artifact: Pace Intent Router v8 | PTLLM](https://posttrainllm.com/artifacts/pace-intent-router-v8) — [Markdown](https://posttrainllm.com/artifacts/pace-intent-router-v8.md) — A 49.5M intent classifier that was extremely fast and strong on its source-matched synthetic holdout, then failed the first leakage-checked sealed… - [Artifact: Parakeet WGSL Browser ASR | PTLLM](https://posttrainllm.com/artifacts/parakeet-wgsl-browser-asr) — [Markdown](https://posttrainllm.com/artifacts/parakeet-wgsl-browser-asr.md) — A pinned Parakeet TDT 0.6B package transcribed 17 minutes of public audio in about eight seconds warm inside Chrome on Apple GPU hardware. - [Artifact: Qwen3-0.6B Routed SQL Specialist | PTLLM](https://posttrainllm.com/artifacts/qwen06-sql-routed-v1) — [Markdown](https://posttrainllm.com/artifacts/qwen06-sql-routed-v1.md) — A two-adapter routed SQL artifact: public schema-only SQL routes to a b-mc2 adapter; local SQLite execution routes to a synthetic execution adapter. - [Artifact: Qwen3-4B File-Ops Distilled | PTLLM](https://posttrainllm.com/artifacts/qwen3-4b-file-ops-distilled) — [Markdown](https://posttrainllm.com/artifacts/qwen3-4b-file-ops-distilled.md) — A routed 4B file-operation specialist distilled from frontier/gold trajectories, with the breadth regression disclosed in the package. - [Artifact: Qwen3-4B Multibackend Distilled | PTLLM](https://posttrainllm.com/artifacts/qwen3-4b-multibackend-distilled) — [Markdown](https://posttrainllm.com/artifacts/qwen3-4b-multibackend-distilled.md) — A public failed attempt that retained 100% file-ops depth but drove the recorded breadth score down to 31%. - [Artifact: Qwen3-4B ReST Fused | PTLLM](https://posttrainllm.com/artifacts/qwen3-4b-rest-fused) — [Markdown](https://posttrainllm.com/artifacts/qwen3-4b-rest-fused.md) — A teacher-free ReST candidate that preserves the 100% file-ops gate while recovering out-of-domain breadth above the stock 4B baseline. - [Artifact: VibeThinker-3B Agentic Distilled | PTLLM](https://posttrainllm.com/artifacts/vibethinker-3b-agentic-distilled) — [Markdown](https://posttrainllm.com/artifacts/vibethinker-3b-agentic-distilled.md) — A preserved agentic distillation checkpoint whose public weights outlived its evaluation evidence; no current win can be claimed. - [Artifact: VibeThinker-3B MLX Conversion | PTLLM](https://posttrainllm.com/artifacts/vibethinker-3b-mlx) — [Markdown](https://posttrainllm.com/artifacts/vibethinker-3b-mlx.md) — A public Apple-Silicon-friendly conversion of WeiboAI/VibeThinker-3B, preserved as a runtime artifact rather than claimed as a PostTrainLLM-trained model. - [Reproducible LLM benchmarks — posttrainllm](https://posttrainllm.com/benchmarks) — [Markdown](https://posttrainllm.com/benchmarks.md) — Browse reproducible language-model benchmarks with frozen protocols, prerecorded outputs, local replay commands, and specialist comparisons. - [Cross-game model arena — posttrainllm](https://posttrainllm.com/benchmarks/arena) — [Markdown](https://posttrainllm.com/benchmarks/arena.md) — Inspect provisional Arena Elo for head-to-head games and paired score evidence for single-player games without conflating the two. - [Character Chess LLM benchmark — posttrainllm](https://posttrainllm.com/benchmarks/chess) — [Markdown](https://posttrainllm.com/benchmarks/chess.md) — Watch frontier, 9B, and 4B language models solve identical chess tactics and replay their complete paired games move by move. - [Character 2048 LLM benchmark — posttrainllm](https://posttrainllm.com/benchmarks/game-2048) — [Markdown](https://posttrainllm.com/benchmarks/game-2048.md) — Inspect the retained Character 2048 benchmark failure, replay every local model move, and see why the expected intelligence advantage did not reproduce. - [How to build a small language model specialist | PTLLM](https://posttrainllm.com/build-small-language-model-specialist) — [Markdown](https://posttrainllm.com/build-small-language-model-specialist.md) — Build a routed small language model specialist with a frozen baseline, narrow training objective, regression gates, packaging, and an honest decision. - [posttrainllm — changelog](https://posttrainllm.com/changelog) — [Markdown](https://posttrainllm.com/changelog.md) — Verified product improvements shipped by the posttrainllm Mac-local specialist factory. - [posttrainllm — devlog](https://posttrainllm.com/devlog) — [Markdown](https://posttrainllm.com/devlog.md) — Notes from building posttrainllm — kernel measurements, honest negative results, decisions made while AI-pairing. - [PostTrainLLM for Mac — release status](https://posttrainllm.com/download) — [Markdown](https://posttrainllm.com/download.md) — Verified release status for the native PostTrainLLM app on Apple Silicon. Downloads remain closed until every macOS trust check passes. - [posttrainllm — eval leaderboard](https://posttrainllm.com/eval-leaderboard) — [Markdown](https://posttrainllm.com/eval-leaderboard.md) — Drop E0 eval JSONL files to compare models, checkpoints, and tasks in the browser. - [How to evaluate a local LLM | PTLLM](https://posttrainllm.com/evaluate-local-llm) — [Markdown](https://posttrainllm.com/evaluate-local-llm.md) — An evidence-first workflow for evaluating a local LLM against a frozen baseline, task gates, regression slices, and an explicit decision. - [Every experiment — posttrainllm](https://posttrainllm.com/experiments) — [Markdown](https://posttrainllm.com/experiments.md) — The complete PostTrainLLM experiment archive: every worked, failed, regressed, inconclusive, superseded, and rejected attempt with evidence and a reusable… - [How to train and fine-tune your own LLM on a Mac | PTLLM](https://posttrainllm.com/fine-tune-llm-on-mac) — [Markdown](https://posttrainllm.com/fine-tune-llm-on-mac.md) — Practical guide to train your own specialist LLM on Apple Silicon: define a target, LoRA/QLoRA post-train, evaluate against a frozen baseline, and package… - [How to train your own LLM (without a GPU cluster) | PTLLM](https://posttrainllm.com/how-to-train-your-own-llm) — [Markdown](https://posttrainllm.com/how-to-train-your-own-llm.md) — What “train your own LLM” usually means in 2026: post-train a base model with LoRA/QLoRA, evaluate honestly, and ship a specialist. Includes Mac-local paths… - [posttrainllm — in-browser inference](https://posttrainllm.com/inference) — [Markdown](https://posttrainllm.com/inference.md) — Run real LLMs fully client-side in your browser via WebGPU (transformers.js). Pull any ONNX model from Hugging Face by id, or pick a curated one — no… - [posttrainllm — leaderboard](https://posttrainllm.com/leaderboard) — [Markdown](https://posttrainllm.com/leaderboard.md) — Browser-trained, browser-runnable LLMs ranked on small benchmarks. Pareto views by params and by training compute. - [Mac-local LLM specialist factory | PostTrainLLM](https://posttrainllm.com/learn) — [Markdown](https://posttrainllm.com/learn.md) — Follow the source-linked loop from target and data through post-training, evaluation, packaging, and an honest ship, retry, or reject report. - [MLX LoRA fine-tuning on Apple Silicon | PTLLM](https://posttrainllm.com/mlx-lora-fine-tuning) — [Markdown](https://posttrainllm.com/mlx-lora-fine-tuning.md) — Learn the MLX LoRA and QLoRA workflow on Apple Silicon, from JSONL data and bounded training to evaluation, fusion, and evidence. - [posttrainllm — train a real transformer in your browser tab](https://posttrainllm.com/playground) — [Markdown](https://posttrainllm.com/playground.md) — Train a real GPT-2 from scratch in your browser tab — 2.6× to 12.1× faster than the multi-threaded WebAssembly baseline thanks to hand-written WebGPU… - [PostTrainLLM vs Axolotl for LLM fine-tuning | PTLLM](https://posttrainllm.com/posttrainllm-vs-axolotl) — [Markdown](https://posttrainllm.com/posttrainllm-vs-axolotl.md) — Compare PostTrainLLM's Apple Silicon specialist-model workflow with Axolotl's configuration-driven LLM fine-tuning framework. - [PostTrainLLM vs Unsloth for local LLM fine-tuning | PTLLM](https://posttrainllm.com/posttrainllm-vs-unsloth) — [Markdown](https://posttrainllm.com/posttrainllm-vs-unsloth.md) — Compare PostTrainLLM's Apple Silicon specialist-model factory with Unsloth's optimized LLM training and inference framework. - [posttrainllm — recipes](https://posttrainllm.com/recipes) — [Markdown](https://posttrainllm.com/recipes.md) — The recipe log: named post-training and packaging recipes for posttrainllm. Each names a failure mode, its data, an eval gate, and a stop rule — not just… - [posttrainllm — the performance journey](https://posttrainllm.com/roadmap) — [Markdown](https://posttrainllm.com/roadmap.md) — The performance journey of posttrainllm — every speed lever, what's shipped, what's blocked, and why. - [posttrainllm — SAE timeline](https://posttrainllm.com/sae-timeline) — [Markdown](https://posttrainllm.com/sae-timeline.md) — Drop a posttrainllm SAE timeline JSONL to see reconstruction error and sparse-feature activity over checkpoints. - [posttrainllm — the speedup curve, in one chart](https://posttrainllm.com/speedup) — [Markdown](https://posttrainllm.com/speedup.md) — WebAssembly SIMD vs hand-written WebGPU on the same GPT-2 model. The speedup is a curve, not a single number: Small 2.6×, Medium 6.8×, Large 9.3×, XL 12.1×… - [posttrainllm — training run dashboard](https://posttrainllm.com/training-dashboard) — [Markdown](https://posttrainllm.com/training-dashboard.md) — Drop a .jsonl training log to see loss, LR, val, step-rate, and spike markers — overlay multiple runs to compare. - [posttrainllm — WebGPU kernel tests](https://posttrainllm.com/webgpu-test) — [Markdown](https://posttrainllm.com/webgpu-test.md) — Mac-local LLM specialist factory — post-training and runtime that fits on one Mac, plus WebGPU playground. ## Fine-tune report cards - [Fine-Tune Report Card — sql-routed-specialist-poc — routed-sft-lora](https://posttrainllm.com/report-cards/qwen06-sql-routed-v1) — [Markdown](https://posttrainllm.com/report-cards/qwen06-sql-routed-v1.md) — Retry — eval: The routed setup is the current best SQL candidate and passes the current public exact plus synthetic execution gates, but it is not a shipped… - [Fine-Tune Report Card — Qwen3-4B File-Ops Distilled](https://posttrainllm.com/report-cards/qwen3-4b-file-ops-distilled) — [Markdown](https://posttrainllm.com/report-cards/qwen3-4b-file-ops-distilled.md) — Shipped: ship only as a routed file-ops specialist; do not use as the general planner - [Fine-Tune Report Card — Qwen3-4B ReST Fused](https://posttrainllm.com/report-cards/qwen3-4b-rest-fused) — [Markdown](https://posttrainllm.com/report-cards/qwen3-4b-rest-fused.md) — Shipped: ship as a research specialist package; do not use as the Pace default planner ## Documentation - [posttrainllm docs technical documentation](https://posttrainllm.com/docs/) — [Markdown](https://posttrainllm.com/docs/index.md) — Mac-local LLM specialist factory — training, inference, evals, systems notes, and learning paths. - [Citations | PostTrainLLM reference](https://posttrainllm.com/docs/CITATIONS/) — [Markdown](https://posttrainllm.com/docs/CITATIONS.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [GRPO-Clarify v1 — RL on the ambig dimension](https://posttrainllm.com/docs/GRPO_CLARIFY/) — [Markdown](https://posttrainllm.com/docs/GRPO_CLARIFY.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Doc map — where things moved | PostTrainLLM reference](https://posttrainllm.com/docs/MAP/) — [Markdown](https://posttrainllm.com/docs/MAP.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [posttrainllm Closure and Fresh-Experiment Gate](https://posttrainllm.com/docs/NEXT/) — [Markdown](https://posttrainllm.com/docs/NEXT.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [posttrainllm — master plan (shipped / skipped / TODO)](https://posttrainllm.com/docs/PLAN/) — [Markdown](https://posttrainllm.com/docs/PLAN.md) — Single source of truth for what's shipped, skipped, and still to build. Consolidated from docs/roadmap/*, docs/progress.md, docs/backlog.md,… - [Project Recommendation Context](https://posttrainllm.com/docs/PROJECT_RECOMMENDATION_CONTEXT/) — [Markdown](https://posttrainllm.com/docs/PROJECT_RECOMMENDATION_CONTEXT.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [posttrainllm Docs technical documentation](https://posttrainllm.com/docs/README/) — [Markdown](https://posttrainllm.com/docs/README.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Agent runtime | PostTrainLLM reference](https://posttrainllm.com/docs/agent_runtime/) — [Markdown](https://posttrainllm.com/docs/agent_runtime.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [How it works: the specialist factory end-to-end](https://posttrainllm.com/docs/architecture/how-it-works/) — [Markdown](https://posttrainllm.com/docs/architecture/how-it-works.md) — A learning-first walkthrough of posttrainllm's Mac-local specialist factory — the target → data → post-training → eval → package → report loop, the native… - [I built a GPT-2 in the browser, then made it 2.6×–12.1×…](https://posttrainllm.com/docs/archive/BLOG/) — [Markdown](https://posttrainllm.com/docs/archive/BLOG.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Session handoff — pick this up cleanly](https://posttrainllm.com/docs/archive/HANDOFF/) — [Markdown](https://posttrainllm.com/docs/archive/HANDOFF.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Milestones | PostTrainLLM reference](https://posttrainllm.com/docs/archive/MILESTONES/) — [Markdown](https://posttrainllm.com/docs/archive/MILESTONES.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [While you slept | PostTrainLLM reference](https://posttrainllm.com/docs/archive/WHILE_YOU_SLEPT/) — [Markdown](https://posttrainllm.com/docs/archive/WHILE_YOU_SLEPT.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Annotated transcript | PostTrainLLM reference](https://posttrainllm.com/docs/archive/annotated_transcript/) — [Markdown](https://posttrainllm.com/docs/archive/annotated_transcript.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Evaluation and safety | PostTrainLLM reference](https://posttrainllm.com/docs/archive/evaluation/) — [Markdown](https://posttrainllm.com/docs/archive/evaluation.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Learning roadmap | PostTrainLLM reference](https://posttrainllm.com/docs/archive/learning_roadmap/) — [Markdown](https://posttrainllm.com/docs/archive/learning_roadmap.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Lessons from building posttrainllm](https://posttrainllm.com/docs/archive/lessons/) — [Markdown](https://posttrainllm.com/docs/archive/lessons.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Parked: the four "multi-model" directions](https://posttrainllm.com/docs/archive/parked_multi_model/) — [Markdown](https://posttrainllm.com/docs/archive/parked_multi_model.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Phase 9 + 10 — status and follow-up design](https://posttrainllm.com/docs/archive/phase_9_10_status/) — [Markdown](https://posttrainllm.com/docs/archive/phase_9_10_status.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Roadmap — recent research (2024-2026 highlights)](https://posttrainllm.com/docs/archive/recent_research/) — [Markdown](https://posttrainllm.com/docs/archive/recent_research.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Shared vs. native — how the browser and macOS paths relate](https://posttrainllm.com/docs/archive/shared_vs_native/) — [Markdown](https://posttrainllm.com/docs/archive/shared_vs_native.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Project status — 2026 update | PostTrainLLM reference](https://posttrainllm.com/docs/archive/status/) — [Markdown](https://posttrainllm.com/docs/archive/status.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Watch the model think | PostTrainLLM reference](https://posttrainllm.com/docs/archive/watch_the_model_think/) — [Markdown](https://posttrainllm.com/docs/archive/watch_the_model_think.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [GitHub Corpus Parity Decision | PostTrainLLM reference](https://posttrainllm.com/docs/artifacts/github-corpus-parity/) — [Markdown](https://posttrainllm.com/docs/artifacts/github-corpus-parity.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Async tool-call dispatch — investigated, skipped](https://posttrainllm.com/docs/async_tool_dispatch/) — [Markdown](https://posttrainllm.com/docs/async_tool_dispatch.md) — Why async tool dispatch in AgentLoop doesn't justify the work at current bottleneck shape. Logged so the decision doesn't get re-litigated. - [Attempt Ledger | PostTrainLLM reference](https://posttrainllm.com/docs/attempt-ledger/) — [Markdown](https://posttrainllm.com/docs/attempt-ledger.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Audit 2026 — what we tried, what worked, what we flagged](https://posttrainllm.com/docs/audits/audit_2026/) — [Markdown](https://posttrainllm.com/docs/audits/audit_2026.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Docs Quality Audit | PostTrainLLM reference](https://posttrainllm.com/docs/audits/docs-quality-audit/) — [Markdown](https://posttrainllm.com/docs/audits/docs-quality-audit.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Exactness Completion Audit | PostTrainLLM reference](https://posttrainllm.com/docs/audits/exactness-completion-audit/) — [Markdown](https://posttrainllm.com/docs/audits/exactness-completion-audit.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Feature audit (2026-05-31) — moved](https://posttrainllm.com/docs/audits/feature_audit_2026_05_31/) — [Markdown](https://posttrainllm.com/docs/audits/feature_audit_2026_05_31.md) — Verification results absorbed into docs/PLAN.md §1. Kept here only as the audit's historical anchor. - [History Coverage Audit | PostTrainLLM reference](https://posttrainllm.com/docs/audits/history-coverage-audit/) — [Markdown](https://posttrainllm.com/docs/audits/history-coverage-audit.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Test Coverage Goals — posttrainllm](https://posttrainllm.com/docs/audits/test-coverage/) — [Markdown](https://posttrainllm.com/docs/audits/test-coverage.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Phase 1-10 validation + end-to-end workflows](https://posttrainllm.com/docs/audits/validation_report/) — [Markdown](https://posttrainllm.com/docs/audits/validation_report.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [ROI-ordered backlog — moved | PostTrainLLM reference](https://posttrainllm.com/docs/backlog/) — [Markdown](https://posttrainllm.com/docs/backlog.md) — Consolidated into docs/PLAN.md §3 (TODO). Tier A/B/C/D ordering preserved. - [BPE-aware leaderboard scoring | PostTrainLLM reference](https://posttrainllm.com/docs/bpe_browser_scoring/) — [Markdown](https://posttrainllm.com/docs/bpe_browser_scoring.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Browser notes — WASM, Workers, OPFS, WebGPU](https://posttrainllm.com/docs/browser_notes/) — [Markdown](https://posttrainllm.com/docs/browser_notes.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Capability matrix — modalities × actions](https://posttrainllm.com/docs/capability_matrix/) — [Markdown](https://posttrainllm.com/docs/capability_matrix.md) — Exhaustive map of input modalities posttrainllm supports and what operations (train, distill, tune, quantize, etc.) are available for each. Honest status per… - [CLI lab reference | PostTrainLLM reference](https://posttrainllm.com/docs/cli-reference/) — [Markdown](https://posttrainllm.com/docs/cli-reference.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Dataset inventory — what's available, sizes, schemas](https://posttrainllm.com/docs/data_inventory/) — [Markdown](https://posttrainllm.com/docs/data_inventory.md) — Reference doc for every dataset wired into posttrainllm — registry entries, what they're for, how to pull them, what the records look like after conversion,… - [Dataset inventory | PostTrainLLM reference](https://posttrainllm.com/docs/dataset-inventory/) — [Markdown](https://posttrainllm.com/docs/dataset-inventory.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [posttrainllm Decision Log technical documentation](https://posttrainllm.com/docs/decision_log/) — [Markdown](https://posttrainllm.com/docs/decision_log.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Document Status Registry | PostTrainLLM reference](https://posttrainllm.com/docs/doc-status/) — [Markdown](https://posttrainllm.com/docs/doc-status.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [External Products And Research Reviewed](https://posttrainllm.com/docs/external-products-reviewed/) — [Markdown](https://posttrainllm.com/docs/external-products-reviewed.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Factory Docs | PostTrainLLM reference](https://posttrainllm.com/docs/factory/README/) — [Markdown](https://posttrainllm.com/docs/factory/README.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Autocorrect adapter recipe and training path](https://posttrainllm.com/docs/factory/autocorrect-adapter-recipe/) — [Markdown](https://posttrainllm.com/docs/factory/autocorrect-adapter-recipe.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Mac-local autocorrect foundation](https://posttrainllm.com/docs/factory/autocorrect-foundation/) — [Markdown](https://posttrainllm.com/docs/factory/autocorrect-foundation.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Autocorrect base-model gate | PostTrainLLM reference](https://posttrainllm.com/docs/factory/autocorrect-model-shortlist/) — [Markdown](https://posttrainllm.com/docs/factory/autocorrect-model-shortlist.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Batch-First Post-Training | PostTrainLLM reference](https://posttrainllm.com/docs/factory/batch-posttraining/) — [Markdown](https://posttrainllm.com/docs/factory/batch-posttraining.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Factory Case Study Template | PostTrainLLM reference](https://posttrainllm.com/docs/factory/case-study-template/) — [Markdown](https://posttrainllm.com/docs/factory/case-study-template.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Factory Enforcement | PostTrainLLM reference](https://posttrainllm.com/docs/factory/enforcement/) — [Markdown](https://posttrainllm.com/docs/factory/enforcement.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Factory Eval Protocol | PostTrainLLM reference](https://posttrainllm.com/docs/factory/eval-protocol/) — [Markdown](https://posttrainllm.com/docs/factory/eval-protocol.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Foundry Evidence Contract — posttrainllm](https://posttrainllm.com/docs/factory/foundry-evidence/) — [Markdown](https://posttrainllm.com/docs/factory/foundry-evidence.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Hugging Face Artifact Storage | PostTrainLLM reference](https://posttrainllm.com/docs/factory/huggingface-artifact-storage/) — [Markdown](https://posttrainllm.com/docs/factory/huggingface-artifact-storage.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [LoRA Geometry Diagnostics | PostTrainLLM reference](https://posttrainllm.com/docs/factory/lora-geometry/) — [Markdown](https://posttrainllm.com/docs/factory/lora-geometry.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Factory Overview | PostTrainLLM reference](https://posttrainllm.com/docs/factory/overview/) — [Markdown](https://posttrainllm.com/docs/factory/overview.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Factory Packaging | PostTrainLLM reference](https://posttrainllm.com/docs/factory/packaging/) — [Markdown](https://posttrainllm.com/docs/factory/packaging.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Post-Training Factory Positioning](https://posttrainllm.com/docs/factory/post-training-factory/) — [Markdown](https://posttrainllm.com/docs/factory/post-training-factory.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Public Artifacts | PostTrainLLM reference](https://posttrainllm.com/docs/factory/public-artifacts/) — [Markdown](https://posttrainllm.com/docs/factory/public-artifacts.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Report Card Cohort and Review | PostTrainLLM reference](https://posttrainllm.com/docs/factory/report-card-cohort/) — [Markdown](https://posttrainllm.com/docs/factory/report-card-cohort.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Fine-Tune Report Card | PostTrainLLM reference](https://posttrainllm.com/docs/factory/report-card/) — [Markdown](https://posttrainllm.com/docs/factory/report-card.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Factory Reports | PostTrainLLM reference](https://posttrainllm.com/docs/factory/reports/) — [Markdown](https://posttrainllm.com/docs/factory/reports.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Factory Run Lifecycle | PostTrainLLM reference](https://posttrainllm.com/docs/factory/run-lifecycle/) — [Markdown](https://posttrainllm.com/docs/factory/run-lifecycle.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Factory Run Schema | PostTrainLLM reference](https://posttrainllm.com/docs/factory/run-schema/) — [Markdown](https://posttrainllm.com/docs/factory/run-schema.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Feature ideas — interactive learning content](https://posttrainllm.com/docs/feature_ideas/) — [Markdown](https://posttrainllm.com/docs/feature_ideas.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Gallery v1.5 and v2 — build plan](https://posttrainllm.com/docs/gallery_v2_plan/) — [Markdown](https://posttrainllm.com/docs/gallery_v2_plan.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Model guide — building posttrainllm from scratch](https://posttrainllm.com/docs/guides/model_guide/) — [Markdown](https://posttrainllm.com/docs/guides/model_guide.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [posttrainllm study guide technical documentation](https://posttrainllm.com/docs/guides/study_guide/) — [Markdown](https://posttrainllm.com/docs/guides/study_guide.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Training guide | PostTrainLLM reference](https://posttrainllm.com/docs/guides/training_guide/) — [Markdown](https://posttrainllm.com/docs/guides/training_guide.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Qwen3-4B File-Ops Distilled — model card](https://posttrainllm.com/docs/hf-cards/qwen3-4b-file-ops-distilled/) — [Markdown](https://posttrainllm.com/docs/hf-cards/qwen3-4b-file-ops-distilled.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Qwen3-4B ReST Fused — model card](https://posttrainllm.com/docs/hf-cards/qwen3-4b-rest-fused/) — [Markdown](https://posttrainllm.com/docs/hf-cards/qwen3-4b-rest-fused.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Industry learning roadmap | PostTrainLLM reference](https://posttrainllm.com/docs/industry_learning_roadmap/) — [Markdown](https://posttrainllm.com/docs/industry_learning_roadmap.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Using posttrainllm with Continue.dev / Cline / Aider](https://posttrainllm.com/docs/integrations/continue_provider/) — [Markdown](https://posttrainllm.com/docs/integrations/continue_provider.md) — posttrainllm exposes an Ollama-compatible HTTP surface so it drops straight into Continue.dev, Cline, and Aider configs as a local provider. Setup + caveats. - [Deploying the playground | PostTrainLLM reference](https://posttrainllm.com/docs/integrations/deploy/) — [Markdown](https://posttrainllm.com/docs/integrations/deploy.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [GitHub data integration | PostTrainLLM reference](https://posttrainllm.com/docs/integrations/github_data_integration/) — [Markdown](https://posttrainllm.com/docs/integrations/github_data_integration.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [HuggingFace Datasets Hub integration](https://posttrainllm.com/docs/integrations/hf_datasets_integration/) — [Markdown](https://posttrainllm.com/docs/integrations/hf_datasets_integration.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [lm-evaluation-harness integration for posttrainllm](https://posttrainllm.com/docs/integrations/lm_eval_integration/) — [Markdown](https://posttrainllm.com/docs/integrations/lm_eval_integration.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Leaderboard — benchmarks for tiny models](https://posttrainllm.com/docs/leaderboard/) — [Markdown](https://posttrainllm.com/docs/leaderboard.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Learn posttrainllm — a guided path for software engineers…](https://posttrainllm.com/docs/learn/) — [Markdown](https://posttrainllm.com/docs/learn.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Learn | PostTrainLLM reference](https://posttrainllm.com/docs/learn/README/) — [Markdown](https://posttrainllm.com/docs/learn/README.md) — posttrainllm's learning corpus — a ground-up curriculum from "what's a neural net" to modern training mechanics, plus reference + research notes. - [Advanced LLM inference and serving — interview-grade map](https://posttrainllm.com/docs/learn/advanced-llm-inference/) — [Markdown](https://posttrainllm.com/docs/learn/advanced-llm-inference.md) — Senior/staff inference-optimization interview topics — KV cache & paging, batching, speculative decoding, quantization, attention kernels/variants, long… - [Advanced LLM training and post-training — interview-grade…](https://posttrainllm.com/docs/learn/advanced-llm-training/) — [Markdown](https://posttrainllm.com/docs/learn/advanced-llm-training.md) — Senior/staff interview topics for LLM training at scale (parallelism, precision, MoE, data) and post-training (RLHF/DPO/GRPO/reward modeling/distillation),… - [Advanced architecture, RAG/agents, eval and ML system…](https://posttrainllm.com/docs/learn/advanced-ml-systems-eval/) — [Markdown](https://posttrainllm.com/docs/learn/advanced-ml-systems-eval.md) — Senior/staff topics spanning modern transformer architecture, the linear-algebra↔transformers whiteboard, RAG, agents, evaluation (LLM-as-judge, perplexity,… - [Agent context as a memory hierarchy](https://posttrainllm.com/docs/learn/agent-context-hierarchy/) — [Markdown](https://posttrainllm.com/docs/learn/agent-context-hierarchy.md) — The L1/L2/L3 framing for agent context engineering — what we stole from the Shortcut vertical-agents essay and where each steal lives in this repo. - [ANE research dossier for posttrainllm](https://posttrainllm.com/docs/learn/ane-research/dossier/) — [Markdown](https://posttrainllm.com/docs/learn/ane-research/dossier.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [M6 — ANE Bisect Findings (2026-06-08)](https://posttrainllm.com/docs/learn/ane-research/m6-findings/) — [Markdown](https://posttrainllm.com/docs/learn/ane-research/m6-findings.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [App Intents comparison for Planner v7](https://posttrainllm.com/docs/learn/app-intents-comparison/) — [Markdown](https://posttrainllm.com/docs/learn/app-intents-comparison.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Apple on-device Foundation Models — what they are, and…](https://posttrainllm.com/docs/learn/apple-on-device-foundation-models/) — [Markdown](https://posttrainllm.com/docs/learn/apple-on-device-foundation-models.md) — What Apple's FoundationModels framework + on-device model give you, the bridge we built to score it on our gates, the measured verdict (can't ground actions;… - [Session 1 — What's a neural net (the building block)](https://posttrainllm.com/docs/learn/archive/session-01-neural-net-basics-dense/) — [Markdown](https://posttrainllm.com/docs/learn/archive/session-01-neural-net-basics-dense.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [The buildable AI journey | PostTrainLLM reference](https://posttrainllm.com/docs/learn/artifact-journey/) — [Markdown](https://posttrainllm.com/docs/learn/artifact-journey.md) — A nine-stage journey from transformer foundations to local specialist models, agents, packages, runtime decisions, and causal probes. - [Castform's RL fine-tune platform — what we stole](https://posttrainllm.com/docs/learn/castform-rl-finetune/) — [Markdown](https://posttrainllm.com/docs/learn/castform-rl-finetune.md) — Patterns lifted from castform.com (RL fine-tune SaaS) into posttrainllm's specialist-training surface. Sibling page to docs/learn/agent-context-hierarchy.md. - [Competitive landscape — fine-tune, eval, interpretability](https://posttrainllm.com/docs/learn/competitive-landscape/) — [Markdown](https://posttrainllm.com/docs/learn/competitive-landscape.md) — A 2026 map of the products around "make a model good at your task" and "is my agent any good," with the Mac-first whitespace called out.… - [Learning coverage map — every subsystem has a home](https://posttrainllm.com/docs/learn/coverage-map/) — [Markdown](https://posttrainllm.com/docs/learn/coverage-map.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [posttrainllm learning curriculum — ground up](https://posttrainllm.com/docs/learn/curriculum/) — [Markdown](https://posttrainllm.com/docs/learn/curriculum.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Diversity-driven small-model reasoning (Spectrum-to-Signal)](https://posttrainllm.com/docs/learn/diversity-driven-small-model-reasoning/) — [Markdown](https://posttrainllm.com/docs/learn/diversity-driven-small-model-reasoning.md) — Three transferable training methods from the VibeThinker papers — Diversity-Exploring Distillation, MaxEnt-Guided Policy Optimization (MGPO), and specialist… - [Encoder-decoder adapters: LoRA on a seq2seq model for text…](https://posttrainllm.com/docs/learn/encoder-decoder-adapters/) — [Markdown](https://posttrainllm.com/docs/learn/encoder-decoder-adapters.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Mathematically essential vs engineering optimization](https://posttrainllm.com/docs/learn/essential-vs-optimization/) — [Markdown](https://posttrainllm.com/docs/learn/essential-vs-optimization.md) — The single most useful lens on this project — which parts compute the model's function (irreducible math, the correctness oracle) vs which parts only make… - [Eval matrix — what every Pace LoRA actually scores](https://posttrainllm.com/docs/learn/eval-matrix-2026-06-08/) — [Markdown](https://posttrainllm.com/docs/learn/eval-matrix-2026-06-08.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Eval methodology — the gate finding](https://posttrainllm.com/docs/learn/eval-methodology-2026-06-08/) — [Markdown](https://posttrainllm.com/docs/learn/eval-methodology-2026-06-08.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [External references — articles worth reading](https://posttrainllm.com/docs/learn/external-references/) — [Markdown](https://posttrainllm.com/docs/learn/external-references.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Learning journal | PostTrainLLM reference](https://posttrainllm.com/docs/learn/journal/) — [Markdown](https://posttrainllm.com/docs/learn/journal.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [How an LLM actually works (the matmul-first explanation)](https://posttrainllm.com/docs/learn/llm-mechanics-fundamentals/) — [Markdown](https://posttrainllm.com/docs/learn/llm-mechanics-fundamentals.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Mac-local AI mastery map — what's buildable, what's built,…](https://posttrainllm.com/docs/learn/mac-mastery-map/) — [Markdown](https://posttrainllm.com/docs/learn/mac-mastery-map.md) — The learning + build agenda for mastering Mac-local AI. Every capability buildable on a single Mac, annotated with fleet coverage (built / partial /… - [Model vs agent — what's actually different](https://posttrainllm.com/docs/learn/model-vs-agent/) — [Markdown](https://posttrainllm.com/docs/learn/model-vs-agent.md) — The architectural distinction between a fine-tuned model and the agent that runs it, and what "making a good model into an agent" actually requires.… - [posttrainllm — study queue technical documentation](https://posttrainllm.com/docs/learn/new-things/) — [Markdown](https://posttrainllm.com/docs/learn/new-things.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Qwen3-VL mRoPE + DeepStack — math spec from HF reference](https://posttrainllm.com/docs/learn/qwen3-vl-mrope-deepstack/) — [Markdown](https://posttrainllm.com/docs/learn/qwen3-vl-mrope-deepstack.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Reproducing Qwen Chess under a 50M-parameter ceiling](https://posttrainllm.com/docs/learn/reproducing-qwen-chess-under-50m/) — [Markdown](https://posttrainllm.com/docs/learn/reproducing-qwen-chess-under-50m.md) — What the external Qwen Chess project actually proves, what posttrainllm can reproduce, and where the 8B-to-44M compression experiment begins. - [RL + multi-autonomous-agents roadmap — the AI game as a…](https://posttrainllm.com/docs/learn/rl-multi-agent-roadmap/) — [Markdown](https://posttrainllm.com/docs/learn/rl-multi-agent-roadmap.md) — Parked blueprint. The fleet's AI game (autonomous characters in a world) is already a multi-agent RL environment — the scarcest RL ingredient. This is the… - [Session 1 — From a line to a learned line](https://posttrainllm.com/docs/learn/session-01-neural-net-basics/) — [Markdown](https://posttrainllm.com/docs/learn/session-01-neural-net-basics.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Session 2 — How the search for `m` and `b` actually works](https://posttrainllm.com/docs/learn/session-02-gradient-descent/) — [Markdown](https://posttrainllm.com/docs/learn/session-02-gradient-descent.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Session 3 — What makes a neural net more than linear…](https://posttrainllm.com/docs/learn/session-03-non-linearities/) — [Markdown](https://posttrainllm.com/docs/learn/session-03-non-linearities.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Session 4 — The taxonomy of ML approaches (and where…](https://posttrainllm.com/docs/learn/session-04-ml-paradigms/) — [Markdown](https://posttrainllm.com/docs/learn/session-04-ml-paradigms.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Session 5 — Scaling: why bigger models know more (and what…](https://posttrainllm.com/docs/learn/session-05-scaling/) — [Markdown](https://posttrainllm.com/docs/learn/session-05-scaling.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Session 6 — Tokenization + embeddings: how text becomes…](https://posttrainllm.com/docs/learn/session-06-tokenization-embeddings/) — [Markdown](https://posttrainllm.com/docs/learn/session-06-tokenization-embeddings.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Session 7 — How models learn behavior (supervised,…](https://posttrainllm.com/docs/learn/session-07-behavior-learning/) — [Markdown](https://posttrainllm.com/docs/learn/session-07-behavior-learning.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Session 8 — Training mechanics: the actual loop](https://posttrainllm.com/docs/learn/session-08-training-mechanics/) — [Markdown](https://posttrainllm.com/docs/learn/session-08-training-mechanics.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Session 9 — Vectors, matrices, tensors: the shapes that…](https://posttrainllm.com/docs/learn/session-09-tensors/) — [Markdown](https://posttrainllm.com/docs/learn/session-09-tensors.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Session 10 — Attention and transformer blocks: routing…](https://posttrainllm.com/docs/learn/session-10-attention/) — [Markdown](https://posttrainllm.com/docs/learn/session-10-attention.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Session 11 — Evals, rewards, and self-improvement: making…](https://posttrainllm.com/docs/learn/session-11-evals-rewards/) — [Markdown](https://posttrainllm.com/docs/learn/session-11-evals-rewards.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Small-model tool-calling: the SOTA playbook (what others do)](https://posttrainllm.com/docs/learn/small-model-tool-calling-playbook/) — [Markdown](https://posttrainllm.com/docs/learn/small-model-tool-calling-playbook.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Speech and systems topics — interview-grade map](https://posttrainllm.com/docs/learn/speech-and-systems-topics/) — [Markdown](https://posttrainllm.com/docs/learn/speech-and-systems-topics.md) — Voice-pipeline latency, WER, speech-to-speech, fine-tuning debugging, feature selection, queues vs websockets, FSDP2 — each mapped to the best external source… - [Tool-calling: how close can a Mac-local small model get to…](https://posttrainllm.com/docs/learn/tool-calling-frontier-parity/) — [Markdown](https://posttrainllm.com/docs/learn/tool-calling-frontier-parity.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [The WebGPU execution model (for reading our shaders)](https://posttrainllm.com/docs/learn/webgpu-execution-model/) — [Markdown](https://posttrainllm.com/docs/learn/webgpu-execution-model.md) — The mental model you need to read posttrainllm's WGSL compute kernels and explain them to another engineer — device/queue, pipeline, dispatch, workgroups,… - [Learning Pipeline | PostTrainLLM reference](https://posttrainllm.com/docs/learning-pipeline/) — [Markdown](https://posttrainllm.com/docs/learning-pipeline.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Learning Progress Tracker | PostTrainLLM reference](https://posttrainllm.com/docs/learning-progress/) — [Markdown](https://posttrainllm.com/docs/learning-progress.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Native macOS app — build plan | PostTrainLLM reference](https://posttrainllm.com/docs/mac_app_plan/) — [Markdown](https://posttrainllm.com/docs/mac_app_plan.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [posttrainllm — a detailed write-up](https://posttrainllm.com/docs/notes/) — [Markdown](https://posttrainllm.com/docs/notes.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Pace landing page — pattern mine + draft](https://posttrainllm.com/docs/pace-landing-draft/) — [Markdown](https://posttrainllm.com/docs/pace-landing-draft.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Pace model manifest — every model role, decided and on disk](https://posttrainllm.com/docs/pace-model-manifest/) — [Markdown](https://posttrainllm.com/docs/pace-model-manifest.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Parked Lanes | PostTrainLLM reference](https://posttrainllm.com/docs/parked/README/) — [Markdown](https://posttrainllm.com/docs/parked/README.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Parked: ANE and CoreML | PostTrainLLM reference](https://posttrainllm.com/docs/parked/ane-coreml/) — [Markdown](https://posttrainllm.com/docs/parked/ane-coreml.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Parked: Broad App Polish | PostTrainLLM reference](https://posttrainllm.com/docs/parked/app-polish/) — [Markdown](https://posttrainllm.com/docs/parked/app-polish.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Parked: Browser and WebGPU | PostTrainLLM reference](https://posttrainllm.com/docs/parked/browser/) — [Markdown](https://posttrainllm.com/docs/parked/browser.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Parked: Tier 5 Research | PostTrainLLM reference](https://posttrainllm.com/docs/parked/tier5/) — [Markdown](https://posttrainllm.com/docs/parked/tier5.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Parked: VLM | PostTrainLLM reference](https://posttrainllm.com/docs/parked/vlm/) — [Markdown](https://posttrainllm.com/docs/parked/vlm.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [First Worked Run — `posttrainllm bench`](https://posttrainllm.com/docs/performance/benchmark_first_run/) — [Markdown](https://posttrainllm.com/docs/performance/benchmark_first_run.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [posttrainllm Inference Benchmark Harness — Design](https://posttrainllm.com/docs/performance/benchmark_harness_design/) — [Markdown](https://posttrainllm.com/docs/performance/benchmark_harness_design.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Cold-start bundle — results | PostTrainLLM reference](https://posttrainllm.com/docs/performance/cold_start_results/) — [Markdown](https://posttrainllm.com/docs/performance/cold_start_results.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [CPU speedup bundle — measured results](https://posttrainllm.com/docs/performance/cpu_speedup_results/) — [Markdown](https://posttrainllm.com/docs/performance/cpu_speedup_results.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [CPU utilization research — what we're leaving on the table](https://posttrainllm.com/docs/performance/cpu_utilization_research/) — [Markdown](https://posttrainllm.com/docs/performance/cpu_utilization_research.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Data-side performance levers | PostTrainLLM reference](https://posttrainllm.com/docs/performance/data_perf/) — [Markdown](https://posttrainllm.com/docs/performance/data_perf.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Determinism contract | PostTrainLLM reference](https://posttrainllm.com/docs/performance/determinism/) — [Markdown](https://posttrainllm.com/docs/performance/determinism.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Flash Attention 2 — backward pass notes](https://posttrainllm.com/docs/performance/fa2_backward_notes/) — [Markdown](https://posttrainllm.com/docs/performance/fa2_backward_notes.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Flash Attention 2 — forward pass notes](https://posttrainllm.com/docs/performance/fa2_forward_notes/) — [Markdown](https://posttrainllm.com/docs/performance/fa2_forward_notes.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Gradient checkpointing — worked example](https://posttrainllm.com/docs/performance/gradient_checkpointing_results/) — [Markdown](https://posttrainllm.com/docs/performance/gradient_checkpointing_results.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [KV Cache Optimization: GQA Audit, In-Place Buffers,…](https://posttrainllm.com/docs/performance/kv_cache_optimization/) — [Markdown](https://posttrainllm.com/docs/performance/kv_cache_optimization.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Memory tradeoffs — bf16, gradient accumulation, gradient…](https://posttrainllm.com/docs/performance/memory_tradeoffs/) — [Markdown](https://posttrainllm.com/docs/performance/memory_tradeoffs.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Online softmax in attention — why and how](https://posttrainllm.com/docs/performance/online_softmax_in_attention/) — [Markdown](https://posttrainllm.com/docs/performance/online_softmax_in_attention.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [MLXFast SDPA and tied-embedding audit (2026-05-30)](https://posttrainllm.com/docs/performance/perf_audit_mlxfast_tied/) — [Markdown](https://posttrainllm.com/docs/performance/perf_audit_mlxfast_tied.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [The browser-frontier performance quest](https://posttrainllm.com/docs/performance/perf_quest/) — [Markdown](https://posttrainllm.com/docs/performance/perf_quest.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Performance research — what's done, what's plausibly next,…](https://posttrainllm.com/docs/performance/perf_research/) — [Markdown](https://posttrainllm.com/docs/performance/perf_research.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Performance notes | PostTrainLLM reference](https://posttrainllm.com/docs/performance/performance/) — [Markdown](https://posttrainllm.com/docs/performance/performance.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [YOCO — "You Only Cache Once" worked example](https://posttrainllm.com/docs/performance/yoco_results/) — [Markdown](https://posttrainllm.com/docs/performance/yoco_results.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [5.1 reasoning training on a 22M model](https://posttrainllm.com/docs/prds/1-reasoning-on-22M/) — [Markdown](https://posttrainllm.com/docs/prds/1-reasoning-on-22M.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [5.2 test-time compute scaling experiment](https://posttrainllm.com/docs/prds/2-testtime-compute-scaling/) — [Markdown](https://posttrainllm.com/docs/prds/2-testtime-compute-scaling.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [5.3 vision-language toy | PostTrainLLM reference](https://posttrainllm.com/docs/prds/3-vision-language-toy/) — [Markdown](https://posttrainllm.com/docs/prds/3-vision-language-toy.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [5.4 diffusion LM micro-implementation](https://posttrainllm.com/docs/prds/4-diffusion-lm-micro/) — [Markdown](https://posttrainllm.com/docs/prds/4-diffusion-lm-micro.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [5.5 real sparse MoE kernels (Metal)](https://posttrainllm.com/docs/prds/5-sparse-moe-kernels/) — [Markdown](https://posttrainllm.com/docs/prds/5-sparse-moe-kernels.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [5.6 TTS toy (audio-token GPT) | PostTrainLLM reference](https://posttrainllm.com/docs/prds/6-tts-toy/) — [Markdown](https://posttrainllm.com/docs/prds/6-tts-toy.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [5.7 specialized explainer-video model (Lamina-like)](https://posttrainllm.com/docs/prds/7-explainer-video-model/) — [Markdown](https://posttrainllm.com/docs/prds/7-explainer-video-model.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [A1 first specialist end-to-end (tool-caller)](https://posttrainllm.com/docs/prds/A1-first-specialist-tool-caller/) — [Markdown](https://posttrainllm.com/docs/prds/A1-first-specialist-tool-caller.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [B1 second specialist (SQL POC)](https://posttrainllm.com/docs/prds/B1-second-specialist-shell-or-sql/) — [Markdown](https://posttrainllm.com/docs/prds/B1-second-specialist-shell-or-sql.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [B10 quality classifier on pretrain data](https://posttrainllm.com/docs/prds/B10-quality-classifier/) — [Markdown](https://posttrainllm.com/docs/prds/B10-quality-classifier.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [B11 WSD (warmup-stable-decay) schedule](https://posttrainllm.com/docs/prds/B11-wsd-schedule/) — [Markdown](https://posttrainllm.com/docs/prds/B11-wsd-schedule.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [B12 loss-spike recovery + replay](https://posttrainllm.com/docs/prds/B12-loss-spike-recovery/) — [Markdown](https://posttrainllm.com/docs/prds/B12-loss-spike-recovery.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [B13 interp-on-checkpoints methodology](https://posttrainllm.com/docs/prds/B13-interp-on-checkpoints/) — [Markdown](https://posttrainllm.com/docs/prds/B13-interp-on-checkpoints.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [B14 speculative decoding (Mini-Llama draft for Mega target)](https://posttrainllm.com/docs/prds/B14-speculative-decoding/) — [Markdown](https://posttrainllm.com/docs/prds/B14-speculative-decoding.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [B15 layer-wise LR decay for SFT](https://posttrainllm.com/docs/prds/B15-layerwise-lr-decay-sft/) — [Markdown](https://posttrainllm.com/docs/prds/B15-layerwise-lr-decay-sft.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [B16 M5 Neural Accelerator prefill benchmark](https://posttrainllm.com/docs/prds/B16-m5-na-prefill-bench/) — [Markdown](https://posttrainllm.com/docs/prds/B16-m5-na-prefill-bench.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [B17 SAE Lens interop / Neuronpedia format export](https://posttrainllm.com/docs/prds/B17-saelens-interop/) — [Markdown](https://posttrainllm.com/docs/prds/B17-saelens-interop.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [B18 nanochat-style `--depth` single-knob HP derivation](https://posttrainllm.com/docs/prds/B18-nanochat-depth-knob/) — [Markdown](https://posttrainllm.com/docs/prds/B18-nanochat-depth-knob.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [B19 Group-SAE (layer-group SAE training)](https://posttrainllm.com/docs/prds/B19-group-sae/) — [Markdown](https://posttrainllm.com/docs/prds/B19-group-sae.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [B2/B2b/B3/B7 mini-router family (bundled)](https://posttrainllm.com/docs/prds/B2-B7-router-family/) — [Markdown](https://posttrainllm.com/docs/prds/B2-B7-router-family.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [B21 Micro-AutoMixer for specialist data mixes](https://posttrainllm.com/docs/prds/B21-micro-automixer/) — [Markdown](https://posttrainllm.com/docs/prds/B21-micro-automixer.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [B23 agent eval protocol hardening](https://posttrainllm.com/docs/prds/B23-agent-eval-protocol/) — [Markdown](https://posttrainllm.com/docs/prds/B23-agent-eval-protocol.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [B25 ScaleDown Challenge specialist](https://posttrainllm.com/docs/prds/B25-scaledown-specialist/) — [Markdown](https://posttrainllm.com/docs/prds/B25-scaledown-specialist.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [B26 server-side deferred tools](https://posttrainllm.com/docs/prds/B26-deferred-tools/) — [Markdown](https://posttrainllm.com/docs/prds/B26-deferred-tools.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [B28 composite reward framework](https://posttrainllm.com/docs/prds/B28-composite-reward-framework/) — [Markdown](https://posttrainllm.com/docs/prds/B28-composite-reward-framework.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [B31 unified model gallery + project-level model pins](https://posttrainllm.com/docs/prds/B31-gallery-and-project-pins/) — [Markdown](https://posttrainllm.com/docs/prds/B31-gallery-and-project-pins.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [B32 `posttrainllm eval` as a CI / pre-commit gate](https://posttrainllm.com/docs/prds/B32-eval-ci-gate/) — [Markdown](https://posttrainllm.com/docs/prds/B32-eval-ci-gate.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [B33 one-command laptop-finetune onboarding](https://posttrainllm.com/docs/prds/B33-laptop-finetune-onboarding/) — [Markdown](https://posttrainllm.com/docs/prds/B33-laptop-finetune-onboarding.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [B34 batched eval-runtime + pluggable MLX backend (oMLX…](https://posttrainllm.com/docs/prds/B34-batched-eval-runtime/) — [Markdown](https://posttrainllm.com/docs/prds/B34-batched-eval-runtime.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [B35 local-agent vertical PoC — code reviewer on a Mac](https://posttrainllm.com/docs/prds/B35-local-agent-vertical-poc/) — [Markdown](https://posttrainllm.com/docs/prds/B35-local-agent-vertical-poc.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [B5 cloud-escalation training signal](https://posttrainllm.com/docs/prds/B5-cloud-escalate-training/) — [Markdown](https://posttrainllm.com/docs/prds/B5-cloud-escalate-training.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [B6 Mac app demo | PostTrainLLM reference](https://posttrainllm.com/docs/prds/B6-mac-app-demo/) — [Markdown](https://posttrainllm.com/docs/prds/B6-mac-app-demo.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [B8 multilingual specialist (Indic-focus first)](https://posttrainllm.com/docs/prds/B8-multilingual-specialist/) — [Markdown](https://posttrainllm.com/docs/prds/B8-multilingual-specialist.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [B9 energy J/token measurement | PostTrainLLM reference](https://posttrainllm.com/docs/prds/B9-energy-per-token/) — [Markdown](https://posttrainllm.com/docs/prds/B9-energy-per-token.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [C10 training-run dashboard | PostTrainLLM reference](https://posttrainllm.com/docs/prds/C10-train-run-dashboard/) — [Markdown](https://posttrainllm.com/docs/prds/C10-train-run-dashboard.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [C3 DoRA on-disk adapter format](https://posttrainllm.com/docs/prds/C3-dora-ondisk-format/) — [Markdown](https://posttrainllm.com/docs/prds/C3-dora-ondisk-format.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [C4 tool-call extractor — BPE tokenizer support](https://posttrainllm.com/docs/prds/C4-tool-extractor-bpe/) — [Markdown](https://posttrainllm.com/docs/prds/C4-tool-extractor-bpe.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [C5 decode jitter under thermal load](https://posttrainllm.com/docs/prds/C5-decode-jitter-thermal/) — [Markdown](https://posttrainllm.com/docs/prds/C5-decode-jitter-thermal.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [C9 determinism harness — bit-exact replay of step N](https://posttrainllm.com/docs/prds/C9-determinism-harness/) — [Markdown](https://posttrainllm.com/docs/prds/C9-determinism-harness.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [E6 `posttrainllm eval-scaledown` harness](https://posttrainllm.com/docs/prds/E6-eval-scaledown/) — [Markdown](https://posttrainllm.com/docs/prds/E6-eval-scaledown.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [GPU / research backlog — what's left, and how to do it](https://posttrainllm.com/docs/prds/GPU-RESEARCH-BACKLOG/) — [Markdown](https://posttrainllm.com/docs/prds/GPU-RESEARCH-BACKLOG.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [PRD Priority Triage | PostTrainLLM reference](https://posttrainllm.com/docs/prds/PRIORITY/) — [Markdown](https://posttrainllm.com/docs/prds/PRIORITY.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [docs/prds/ — Product Requirement Briefs](https://posttrainllm.com/docs/prds/README/) — [Markdown](https://posttrainllm.com/docs/prds/README.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [PRD status — code-verified audit (2026-06-20)](https://posttrainllm.com/docs/prds/STATUS/) — [Markdown](https://posttrainllm.com/docs/prds/STATUS.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Capability retention under fine-tuning (PRD — backlog)](https://posttrainllm.com/docs/prds/capability-retention/) — [Markdown](https://posttrainllm.com/docs/prds/capability-retention.md) — Measure and preserve a small model's GENERAL intelligence when we specialize it (tool-calling, style, persona). This session proved specialization erodes… - [PRD — On-device continual-learning loop](https://posttrainllm.com/docs/prds/continual-learning-loop/) — [Markdown](https://posttrainllm.com/docs/prds/continual-learning-loop.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Everyday specialist benchmark | PostTrainLLM reference](https://posttrainllm.com/docs/prds/everyday-specialist-benchmark/) — [Markdown](https://posttrainllm.com/docs/prds/everyday-specialist-benchmark.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Factory completeness tracker — meta-PRD](https://posttrainllm.com/docs/prds/factory-completeness-tracker/) — [Markdown](https://posttrainllm.com/docs/prds/factory-completeness-tracker.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Planner v7 — tools-in-prompt (generalizable function…](https://posttrainllm.com/docs/prds/factory-planner-v7-tools-in-prompt/) — [Markdown](https://posttrainllm.com/docs/prds/factory-planner-v7-tools-in-prompt.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [VLM M4 architecture decision — Qwen3-VL port vs LLaVA…](https://posttrainllm.com/docs/prds/factory-vision-m4-architecture-decision/) — [Markdown](https://posttrainllm.com/docs/prds/factory-vision-m4-architecture-decision.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [VLM M4 — Qwen3-VL implementation plan](https://posttrainllm.com/docs/prds/factory-vision-m4-impl-plan/) — [Markdown](https://posttrainllm.com/docs/prds/factory-vision-m4-impl-plan.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Vision-language specialist for Pace screen reading](https://posttrainllm.com/docs/prds/factory-vision-specialist/) — [Markdown](https://posttrainllm.com/docs/prds/factory-vision-specialist.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Game-as-RL-environment PoC (PRD)](https://posttrainllm.com/docs/prds/game-rl-environment-poc/) — [Markdown](https://posttrainllm.com/docs/prds/game-rl-environment-poc.md) — Turn the fleet's AI game (autonomous characters in a world) into a Mac-local RL environment and train a self-improving NPC with GRPO. Environments are the… - [GEPA prompt evolution — automated system-prompt iteration](https://posttrainllm.com/docs/prds/gepa-prompt-evolution/) — [Markdown](https://posttrainllm.com/docs/prds/gepa-prompt-evolution.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Local-model arena — turn-based strategy, self-play RLVR…](https://posttrainllm.com/docs/prds/local-model-arena-selfplay/) — [Markdown](https://posttrainllm.com/docs/prds/local-model-arena-selfplay.md) — Pit Mac-local models against each other and against frontier models in turn-based strategy games, then self-play-RL a local model until it beats a frontier… - [Port macOS 26 int8 direct ANE array handoff into M8 chain](https://posttrainllm.com/docs/prds/macos26-int8-ane-handoff-port/) — [Markdown](https://posttrainllm.com/docs/prds/macos26-int8-ane-handoff-port.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Multi-turn / agentic tool-calling eval (PRD)](https://posttrainllm.com/docs/prds/multi-turn-agentic-eval/) — [Markdown](https://posttrainllm.com/docs/prds/multi-turn-agentic-eval.md) — A Mac-local, frontier-gated, STATEFUL multi-turn eval — the single biggest blind spot in our tool-calling work. Single-turn numbers (88.7 for our best 4B)… - [Pace planner v11 — ship gate | PostTrainLLM reference](https://posttrainllm.com/docs/prds/pace-planner-v11-ship-gate/) — [Markdown](https://posttrainllm.com/docs/prds/pace-planner-v11-ship-gate.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Pace planner v11 — unhappy-path training data PRD](https://posttrainllm.com/docs/prds/pace-planner-v11-training-data/) — [Markdown](https://posttrainllm.com/docs/prds/pace-planner-v11-training-data.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Pace Task Loop v1 — long-horizon tasks + the bulk-download…](https://posttrainllm.com/docs/prds/pace-task-loop-v1/) — [Markdown](https://posttrainllm.com/docs/prds/pace-task-loop-v1.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [QLoRA on Mac — fine-tuning larger open models on 48 GB](https://posttrainllm.com/docs/prds/qlora-large-model-finetune/) — [Markdown](https://posttrainllm.com/docs/prds/qlora-large-model-finetune.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Quantized inference in posttrainllm's HFModel — Swift-side…](https://posttrainllm.com/docs/prds/quantized-inference-swift/) — [Markdown](https://posttrainllm.com/docs/prds/quantized-inference-swift.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Self-improving agents — the closed loop (PRD)](https://posttrainllm.com/docs/prds/self-improving-agents/) — [Markdown](https://posttrainllm.com/docs/prds/self-improving-agents.md) — A verifiable act, score, learn, and curriculum loop for testing whether a Mac-local agent can improve without a teacher. - [Specialist capability graph and verified cascade](https://posttrainllm.com/docs/prds/specialist-capability-graph/) — [Markdown](https://posttrainllm.com/docs/prds/specialist-capability-graph.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Pace planner specialist — first factory customer](https://posttrainllm.com/docs/prds/specialist-pace-planner/) — [Markdown](https://posttrainllm.com/docs/prds/specialist-pace-planner.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [posttrainllm product thesis — the embedded local-AI…](https://posttrainllm.com/docs/prds/tinygpt-product-thesis/) — [Markdown](https://posttrainllm.com/docs/prds/tinygpt-product-thesis.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [VLM A/B — UI-Venus-1.5-2B vs Qwen3-VL-2B for Pace's vision…](https://posttrainllm.com/docs/prds/vlm-ab-uivenus-vs-qwen3vl/) — [Markdown](https://posttrainllm.com/docs/prds/vlm-ab-uivenus-vs-qwen3vl.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Progress dashboard — moved | PostTrainLLM reference](https://posttrainllm.com/docs/progress/) — [Markdown](https://posttrainllm.com/docs/progress.md) — Consolidated into docs/PLAN.md §1 (shipped). Headline metrics live there. - [Quality Bundle: tests, lint, crash recovery](https://posttrainllm.com/docs/quality_bundle/) — [Markdown](https://posttrainllm.com/docs/quality_bundle.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [`posttrainllm quickstart` — your first specialist in one…](https://posttrainllm.com/docs/quickstart/) — [Markdown](https://posttrainllm.com/docs/quickstart.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [posttrainllm Recipes technical documentation](https://posttrainllm.com/docs/recipes/README/) — [Markdown](https://posttrainllm.com/docs/recipes/README.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Recipe — micro-AutoMixer for data-mix ratios (B21)](https://posttrainllm.com/docs/recipes/automix/) — [Markdown](https://posttrainllm.com/docs/recipes/automix.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Recipe — B25 ScaleDown specialist (extractive context…](https://posttrainllm.com/docs/recipes/b25-scaledown/) — [Markdown](https://posttrainllm.com/docs/recipes/b25-scaledown.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Balanced training mix via reasoning-depth classification](https://posttrainllm.com/docs/recipes/balanced-training-mix/) — [Markdown](https://posttrainllm.com/docs/recipes/balanced-training-mix.md) — Use B30 ahead of B29 to control single-hop / multi-hop / comparison ratios in the SFT corpus. - [Cookbook - Character Specialist](https://posttrainllm.com/docs/recipes/cookbook-character-specialist/) — [Markdown](https://posttrainllm.com/docs/recipes/cookbook-character-specialist.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Cookbook - Personal Code Specialist](https://posttrainllm.com/docs/recipes/cookbook-personal-code-specialist/) — [Markdown](https://posttrainllm.com/docs/recipes/cookbook-personal-code-specialist.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Cookbook - posttrainllm with Pydantic AI](https://posttrainllm.com/docs/recipes/cookbook-pydantic-ai/) — [Markdown](https://posttrainllm.com/docs/recipes/cookbook-pydantic-ai.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Cookbook - posttrainllm with smolagents](https://posttrainllm.com/docs/recipes/cookbook-smolagents/) — [Markdown](https://posttrainllm.com/docs/recipes/cookbook-smolagents.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Recipe: distill a big model into a small local specialist](https://posttrainllm.com/docs/recipes/distill-specialist/) — [Markdown](https://posttrainllm.com/docs/recipes/distill-specialist.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Recipe — distill function-calling from Phi-3-mini →…](https://posttrainllm.com/docs/recipes/distillation-fc/) — [Markdown](https://posttrainllm.com/docs/recipes/distillation-fc.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Eval gate (CI / pre-commit) | PostTrainLLM reference](https://posttrainllm.com/docs/recipes/eval-gate/) — [Markdown](https://posttrainllm.com/docs/recipes/eval-gate.md) — Gate a posttrainllm specialist in CI — `posttrainllm eval-gate` exits non-zero when any declared eval suite regresses past threshold, on a self-hosted Mac… - [Recipe - Pace Planner Eval Protocol](https://posttrainllm.com/docs/recipes/eval_planner/) — [Markdown](https://posttrainllm.com/docs/recipes/eval_planner.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [From .atraj rollouts to a trained specialist](https://posttrainllm.com/docs/recipes/from-traces/) — [Markdown](https://posttrainllm.com/docs/recipes/from-traces.md) — B22 + B29 + B30 + posttrainllm sft chained — the closed substrate→training loop, V1. - [MLX Export | PostTrainLLM reference](https://posttrainllm.com/docs/recipes/mlx-export/) — [Markdown](https://posttrainllm.com/docs/recipes/mlx-export.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Recipe — Pace planner specialist](https://posttrainllm.com/docs/recipes/pace-planner/) — [Markdown](https://posttrainllm.com/docs/recipes/pace-planner.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [B20 — Learnable cross-stream attention evaluation](https://posttrainllm.com/docs/research/cross-stream-attention-evaluation/) — [Markdown](https://posttrainllm.com/docs/research/cross-stream-attention-evaluation.md) — Read-and-evaluate write-up on the modded-nanogpt speedrun's "cross-stream attention" trick; decide whether to adopt for posttrainllm. - [Indic-language evals — MILU + IndicGenBench wiring](https://posttrainllm.com/docs/research/indic_evals/) — [Markdown](https://posttrainllm.com/docs/research/indic_evals.md) — How posttrainllm scores Indic-language ability — MILU multi-choice across 11 langs, IndicGenBench XQuAD extractive QA. The Wave 4 gate before claiming Hindi… - [State of LLM Inference Benchmarks (May 2026)](https://posttrainllm.com/docs/research/inference_benchmarks_may_2026/) — [Markdown](https://posttrainllm.com/docs/research/inference_benchmarks_may_2026.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Mac inference baseline — M5 Pro / 48GB](https://posttrainllm.com/docs/research/mac_decode_baseline_m5pro/) — [Markdown](https://posttrainllm.com/docs/research/mac_decode_baseline_m5pro.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Mac SLM agentic leaderboard v0](https://posttrainllm.com/docs/research/mac_slm_leaderboard_v0/) — [Markdown](https://posttrainllm.com/docs/research/mac_slm_leaderboard_v0.md) — One artifact that cross-cuts decode speed, BFCL, τ-bench, and Pace unhappy-paths — the publication-shape view we're missing. - [LLM Quality Benchmark Landscape, May 2026 — Survey for…](https://posttrainllm.com/docs/research/quality_benchmarks_may_2026/) — [Markdown](https://posttrainllm.com/docs/research/quality_benchmarks_may_2026.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Wave 2.5 kernel audit — BUILD / DEFER / DROP](https://posttrainllm.com/docs/research/wave_2_5_kernel_audit/) — [Markdown](https://posttrainllm.com/docs/research/wave_2_5_kernel_audit.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Wave 4 landscape — TML / Apple FM / code agents / Indic](https://posttrainllm.com/docs/research/wave_4_landscape/) — [Markdown](https://posttrainllm.com/docs/research/wave_4_landscape.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Single-machine roadmap — moved](https://posttrainllm.com/docs/roadmap/) — [Markdown](https://posttrainllm.com/docs/roadmap.md) — Consolidated into docs/PLAN.md. This preserved page points to the current PostTrainLLM roadmap and canonical project status. - [Roadmap blockers — moved | PostTrainLLM reference](https://posttrainllm.com/docs/roadmap/blockers/) — [Markdown](https://posttrainllm.com/docs/roadmap/blockers.md) — Consolidated into docs/PLAN.md §2. This preserved page points to the current PostTrainLLM roadmap and canonical project status. - [Roadmap orthogonal categories — moved](https://posttrainllm.com/docs/roadmap/categories/) — [Markdown](https://posttrainllm.com/docs/roadmap/categories.md) — Consolidated into docs/PLAN.md. Several markers in the old version were stale; the merged doc is verified against code. - [Roadmap — open-source datasets](https://posttrainllm.com/docs/roadmap/datasets/) — [Markdown](https://posttrainllm.com/docs/roadmap/datasets.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Roadmap honest summary — moved](https://posttrainllm.com/docs/roadmap/honest_summary/) — [Markdown](https://posttrainllm.com/docs/roadmap/honest_summary.md) — Consolidated into docs/PLAN.md. This preserved page points to the current PostTrainLLM roadmap and canonical project status. - [North Star — refined (2026-05-31)](https://posttrainllm.com/docs/roadmap/north_star_refined/) — [Markdown](https://posttrainllm.com/docs/roadmap/north_star_refined.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Roadmap phased plan — moved | PostTrainLLM reference](https://posttrainllm.com/docs/roadmap/phased_plan/) — [Markdown](https://posttrainllm.com/docs/roadmap/phased_plan.md) — Consolidated into docs/PLAN.md. Most phases shipped; remainder in §3. This preserved page points to the current PostTrainLLM roadmap and canonical project… - [Roadmap recommended order — moved](https://posttrainllm.com/docs/roadmap/recommended_order/) — [Markdown](https://posttrainllm.com/docs/roadmap/recommended_order.md) — Consolidated into docs/PLAN.md §3. This preserved page points to the current PostTrainLLM roadmap and canonical project status. - [Roadmap Tier 1 — moved | PostTrainLLM reference](https://posttrainllm.com/docs/roadmap/tier1/) — [Markdown](https://posttrainllm.com/docs/roadmap/tier1.md) — Consolidated into docs/PLAN.md. This preserved page points to the current PostTrainLLM roadmap and canonical project status. - [Roadmap Tier 2 — moved | PostTrainLLM reference](https://posttrainllm.com/docs/roadmap/tier2/) — [Markdown](https://posttrainllm.com/docs/roadmap/tier2.md) — Consolidated into docs/PLAN.md. This preserved page points to the current PostTrainLLM roadmap and canonical project status. - [Roadmap Tier 3 — moved | PostTrainLLM reference](https://posttrainllm.com/docs/roadmap/tier3/) — [Markdown](https://posttrainllm.com/docs/roadmap/tier3.md) — Consolidated into docs/PLAN.md. This preserved page points to the current PostTrainLLM roadmap and canonical project status. - [Roadmap Tier 4 (skip) — moved | PostTrainLLM reference](https://posttrainllm.com/docs/roadmap/tier4_skip/) — [Markdown](https://posttrainllm.com/docs/roadmap/tier4_skip.md) — Consolidated into docs/PLAN.md §2. This preserved page points to the current PostTrainLLM roadmap and canonical project status. - [Roadmap Tier 5 (2026 frontier) — moved](https://posttrainllm.com/docs/roadmap/tier5_frontier_2026/) — [Markdown](https://posttrainllm.com/docs/roadmap/tier5_frontier_2026.md) — Consolidated into docs/PLAN.md §3 Tier 5. This preserved page points to the current PostTrainLLM roadmap and canonical project status. - [Session — eval-first prep before the 2-day training window](https://posttrainllm.com/docs/sessions/06-05-eval-first/) — [Markdown](https://posttrainllm.com/docs/sessions/06-05-eval-first.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Strategy session — what posttrainllm actually is, what's…](https://posttrainllm.com/docs/sessions/06-06-mac-specialist-platform/) — [Markdown](https://posttrainllm.com/docs/sessions/06-06-mac-specialist-platform.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Specialist Embedder PRD Blocker](https://posttrainllm.com/docs/sessions/06-06-specialist-embedder-blocker/) — [Markdown](https://posttrainllm.com/docs/sessions/06-06-specialist-embedder-blocker.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [vllm-mlx Investigation | PostTrainLLM reference](https://posttrainllm.com/docs/sessions/06-06-vllm-mlx-investigation/) — [Markdown](https://posttrainllm.com/docs/sessions/06-06-vllm-mlx-investigation.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Session — 2026-06-08, the factory-validation night](https://posttrainllm.com/docs/sessions/06-08-factory-validation-night/) — [Markdown](https://posttrainllm.com/docs/sessions/06-08-factory-validation-night.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Strategy session — the market landscape and the Mac-first…](https://posttrainllm.com/docs/sessions/06-13-market-landscape-mac-first/) — [Markdown](https://posttrainllm.com/docs/sessions/06-13-market-landscape-mac-first.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Session — from frontier-parity distillation to the…](https://posttrainllm.com/docs/sessions/06-16-distill-to-self-improvement/) — [Markdown](https://posttrainllm.com/docs/sessions/06-16-distill-to-self-improvement.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Step-back: what we have, the wall, and the ROI menu](https://posttrainllm.com/docs/sessions/06-17-stepback-inventory-roi/) — [Markdown](https://posttrainllm.com/docs/sessions/06-17-stepback-inventory-roi.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Vercel Eve and the local-agent wedge](https://posttrainllm.com/docs/sessions/06-17-vercel-eve-and-local-agents/) — [Markdown](https://posttrainllm.com/docs/sessions/06-17-vercel-eve-and-local-agents.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Drilldown — the experiments left untried, run to bedrock](https://posttrainllm.com/docs/sessions/DRILLDOWN/) — [Markdown](https://posttrainllm.com/docs/sessions/DRILLDOWN.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [posttrainllm — retrospective and forward plan](https://posttrainllm.com/docs/sessions/RETROSPECTIVE/) — [Markdown](https://posttrainllm.com/docs/sessions/RETROSPECTIVE.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Pace handoff — posttrainllm deliverables status (2026-06-10)](https://posttrainllm.com/docs/sessions/pace-handoff-2026-06-10/) — [Markdown](https://posttrainllm.com/docs/sessions/pace-handoff-2026-06-10.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Planner lock - 2026-06-19 | PostTrainLLM reference](https://posttrainllm.com/docs/sessions/planner-lock-2026-06-19/) — [Markdown](https://posttrainllm.com/docs/sessions/planner-lock-2026-06-19.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [posttrainllm — Working Session QandA Log](https://posttrainllm.com/docs/sessions/qa_log/) — [Markdown](https://posttrainllm.com/docs/sessions/qa_log.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Session retrospective — 2026-05-31](https://posttrainllm.com/docs/sessions/session_2026_05_31/) — [Markdown](https://posttrainllm.com/docs/sessions/session_2026_05_31.md) — Multi-hour session that took posttrainllm from "Wave 2.5 stalled" to "every CLI verified + first specialist trained end-to-end +… - [Session Retrospective: posttrainllm, the Long Working…](https://posttrainllm.com/docs/sessions/session_retrospective/) — [Markdown](https://posttrainllm.com/docs/sessions/session_retrospective.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [First specialist run — toolcall-v1 findings](https://posttrainllm.com/docs/sessions/specialist_v1_findings/) — [Markdown](https://posttrainllm.com/docs/sessions/specialist_v1_findings.md) — End-to-end first specialist training on M5 Pro. What worked, what didn't, what was unblocked, and what to try next. - [v11 baselines — committed 2026-06-09](https://posttrainllm.com/docs/sessions/v11-baselines-2026-06-09/) — [Markdown](https://posttrainllm.com/docs/sessions/v11-baselines-2026-06-09.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [WWDC 2026 (June 8–9) — impact on posttrainllm + Pace](https://posttrainllm.com/docs/sessions/wwdc-2026-impact/) — [Markdown](https://posttrainllm.com/docs/sessions/wwdc-2026-impact.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [A1 — first tool-calling specialist](https://posttrainllm.com/docs/specialists/a1-tool-caller/) — [Markdown](https://posttrainllm.com/docs/specialists/a1-tool-caller.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [B1 SQL POC | PostTrainLLM reference](https://posttrainllm.com/docs/specialists/b1-sql-poc/) — [Markdown](https://posttrainllm.com/docs/specialists/b1-sql-poc.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [B1 — second specialist: text-to-SQL](https://posttrainllm.com/docs/specialists/b1-sql/) — [Markdown](https://posttrainllm.com/docs/specialists/b1-sql.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Technique Registry | PostTrainLLM reference](https://posttrainllm.com/docs/techniques/README/) — [Markdown](https://posttrainllm.com/docs/techniques/README.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Audit 2026 Technique Inventory](https://posttrainllm.com/docs/techniques/audit-inventory/) — [Markdown](https://posttrainllm.com/docs/techniques/audit-inventory.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Constrained generation: JSON-mode for posttrainllm](https://posttrainllm.com/docs/techniques/constrained_generation/) — [Markdown](https://posttrainllm.com/docs/techniques/constrained_generation.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Knowledge distillation — making a tiny model punch above…](https://posttrainllm.com/docs/techniques/distillation/) — [Markdown](https://posttrainllm.com/docs/techniques/distillation.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Evolution Strategies — gradient-free training](https://posttrainllm.com/docs/techniques/evolution_strategies/) — [Markdown](https://posttrainllm.com/docs/techniques/evolution_strategies.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [GaLore + training-stability bells (Tier 2)](https://posttrainllm.com/docs/techniques/galore_and_stability/) — [Markdown](https://posttrainllm.com/docs/techniques/galore_and_stability.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Interpretability tools — what is the model thinking?](https://posttrainllm.com/docs/techniques/interpretability/) — [Markdown](https://posttrainllm.com/docs/techniques/interpretability.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [LoRA guide — fine-tuning the tiny model](https://posttrainllm.com/docs/techniques/lora_guide/) — [Markdown](https://posttrainllm.com/docs/techniques/lora_guide.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Method vs Recipe | PostTrainLLM reference](https://posttrainllm.com/docs/techniques/method-vs-recipe/) — [Markdown](https://posttrainllm.com/docs/techniques/method-vs-recipe.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Mixture-of-Experts — more capacity per byte of weight](https://posttrainllm.com/docs/techniques/moe/) — [Markdown](https://posttrainllm.com/docs/techniques/moe.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Multi-Token Prediction — better training signal per step](https://posttrainllm.com/docs/techniques/mtp/) — [Markdown](https://posttrainllm.com/docs/techniques/mtp.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Needle 2 baseline review | PostTrainLLM reference](https://posttrainllm.com/docs/techniques/needle2-baseline-review/) — [Markdown](https://posttrainllm.com/docs/techniques/needle2-baseline-review.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Optimizers | PostTrainLLM reference](https://posttrainllm.com/docs/techniques/optimizers/) — [Markdown](https://posttrainllm.com/docs/techniques/optimizers.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [parakeet.wgsl browser smoke | PostTrainLLM reference](https://posttrainllm.com/docs/techniques/parakeet-wgsl-browser-smoke/) — [Markdown](https://posttrainllm.com/docs/techniques/parakeet-wgsl-browser-smoke.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [PEFT Variants in posttrainllm technical documentation](https://posttrainllm.com/docs/techniques/peft_variants/) — [Markdown](https://posttrainllm.com/docs/techniques/peft_variants.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Numerics and precision — the gate framework](https://posttrainllm.com/docs/techniques/precision/) — [Markdown](https://posttrainllm.com/docs/techniques/precision.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Pruning — magnitude masks and structured surgery](https://posttrainllm.com/docs/techniques/pruning/) — [Markdown](https://posttrainllm.com/docs/techniques/pruning.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Quantization Expansion | PostTrainLLM reference](https://posttrainllm.com/docs/techniques/quantization_expansion/) — [Markdown](https://posttrainllm.com/docs/techniques/quantization_expansion.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Speculative-decoding heads: Medusa + EAGLE-2](https://posttrainllm.com/docs/techniques/speculative_heads/) — [Markdown](https://posttrainllm.com/docs/techniques/speculative_heads.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [SQL Technique Lineage — Closed](https://posttrainllm.com/docs/techniques/sql-technique-backlog/) — [Markdown](https://posttrainllm.com/docs/techniques/sql-technique-backlog.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [StreamingLLM + KIVI — long-context decode without growing…](https://posttrainllm.com/docs/techniques/streaming_llm_kivi/) — [Markdown](https://posttrainllm.com/docs/techniques/streaming_llm_kivi.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [TrainLoop Teardown | PostTrainLLM reference](https://posttrainllm.com/docs/techniques/trainloop-teardown/) — [Markdown](https://posttrainllm.com/docs/techniques/trainloop-teardown.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Tool-call extractor (mini-router)](https://posttrainllm.com/docs/tool_call_extractor/) — [Markdown](https://posttrainllm.com/docs/tool_call_extractor.md) — Design + training recipe for posttrainllm's tiny encoder model that picks which tool a user query needs, before the full LM forward pass. - [The three phases of training — pretrain, SFT, DPO](https://posttrainllm.com/docs/training/) — [Markdown](https://posttrainllm.com/docs/training.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [DPO training: direct preference optimization for LLMs](https://posttrainllm.com/docs/training/dpo/) — [Markdown](https://posttrainllm.com/docs/training/dpo.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [posttrainllm — nightly training queue](https://posttrainllm.com/docs/training/nightly/) — [Markdown](https://posttrainllm.com/docs/training/nightly.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Pretraining | PostTrainLLM reference](https://posttrainllm.com/docs/training/pretrain/) — [Markdown](https://posttrainllm.com/docs/training/pretrain.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths. - [Supervised fine-tuning (SFT) | PostTrainLLM reference](https://posttrainllm.com/docs/training/sft/) — [Markdown](https://posttrainllm.com/docs/training/sft.md) — Mac-local LLM factory documentation — training, inference, evals, systems notes, and learning paths.