Six published models · six case studies

Every public model, with the result and the failure mode.

Every PostTrainLLM model on Hugging Face now has a first-class case study, including routed wins, a sealed-eval rejection, a negative-transfer failure, a conversion-only release, and an unevaluated checkpoint. Hub request counters are not treated as users or adoption. The rest of the registry preserves benchmark reports and process artifacts.

6published model case studies
14tracked public artifacts
2parked but public

Current Release Surface

SQL factory POC

Report-ready candidate

Qwen3-0.6B Routed SQL Specialist

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.

Public exact
0.531
T5-small baseline
0.484
Synthetic execution
0.860

Competition: posttrainllm routed SQL v1 vs T5-small local baseline

Next: Publish as a report artifact. Two hygiene DPO retries proved reference anchoring cures the SimPO collapse and even lifts execution to 0.920, but output hygiene needs a generation-strength fix (stronger SFT or constrained SELECT-prefix decoding), not more preference tuning. Do not present as a shipped SQL model until a public execution gate and clean-output gate pass.

Report only — no model to use not fully verified Report card →

49.5M on-device classifier

Release-ready weights

Pace Intent Router v8

A 49.5M intent classifier that was extremely fast and strong on its source-matched synthetic holdout, then failed the first leakage-checked sealed distribution gate.

Sealed accuracy
57.1%
Frontier retained
57.1%
Warm latency
3.8ms

Competition: Pace Intent Router v8 vs Qwen3-4B-Instruct-2507 4-bit

Next: Do not tune on sealed V1. Build a broader public-development corpus from the failure themes, train a successor, and judge it once on a newly generated sealed V2.

First specialist package

Release-ready weights

Qwen3-4B File-Ops Distilled

A routed 4B file-operation specialist distilled from frontier/gold trajectories, with the breadth regression disclosed in the package.

File-ops hard gate
100%
Heldout file-ops
95%
Breadth after tuning
42.3%

Competition: posttrainllm Qwen3-4B file-ops specialist vs Stock Qwen3-4B

Next: Keep routed-only warnings attached and add a consumer pull/load smoke before wiring this into any app.

Shipped — routed only not fully verified Report card →

Teacher-free breadth recovery

Release-ready weights

Qwen3-4B ReST Fused

A teacher-free ReST candidate that preserves the 100% file-ops gate while recovering out-of-domain breadth above the stock 4B baseline.

File-ops hard gate
100%
Breadth after ReST
65%
Breadth delta
+5.4pp

Competition: posttrainllm Qwen3-4B ReST vs Stock Qwen3-4B

Next: Keep this package research-only. Freeze a product-specific target before spending compute on another eval or training run.

Shipped — routed only not fully verified Report card →

Negative-transfer case study

Report artifact

Qwen3-4B Multibackend Distilled

A public failed attempt that retained 100% file-ops depth but drove the recorded breadth score down to 31%.

File-ops depth
100%
Breadth
31%
Breadth delta
-28.6pp

Competition: Multibackend-distilled 4B vs Stock Qwen3-4B

Next: Keep the weights public as a failed comparison artifact. Do not spend compute revalidating them unless a new breadth-preserving recipe explicitly needs this checkpoint as its baseline.

Conversion case study

Report artifact

VibeThinker-3B MLX Conversion

A public Apple-Silicon-friendly conversion of WeiboAI/VibeThinker-3B, preserved as a runtime artifact rather than claimed as a PostTrainLLM-trained model.

Model class
3B
Local GSM8K screen
40/40
Training delta
None

Competition: PostTrainLLM VibeThinker-3B MLX vs WeiboAI/VibeThinker-3B

Next: Keep this as a conversion and runtime artifact. Add a pinned loader plus parity receipt only when a real Mac-local consumer justifies maintaining it.

Missing-evidence case study

Blocked

VibeThinker-3B Agentic Distilled

A preserved agentic distillation checkpoint whose public weights outlived its evaluation evidence; no current win can be claimed.

Public weights
Yes
Current agentic eval
Missing
Reasoning retention
Unknown

Competition: VibeThinker-3B agentic distilled vs VibeThinker-3B MLX base

Next: Leave the weights public but unpromoted. If this lineage becomes active again, start with an evidence-only baseline/candidate evaluation—not more training.

Artifact storage cleanup

Report artifact

Hugging Face Specialist Model Archive v1

The local specialist model cache was promoted to Hugging Face or deleted when safely re-downloadable from upstream repos.

posttrainllm HF repos
6
Local model cache
cleared
Storage policy
HF first

Competition: Hugging Face artifact storage vs Local Mac cache

Next: Use this as the storage index; use the six dedicated model case studies for quality and decision evidence.

Process artifact

Report artifact

Factory Run Schema v1

The canonical target -> data -> post-training -> eval -> package -> report shape for posttrainllm runs.

Required files
8
Decisions
6
First-class outputs
5

Competition: posttrainllm factory schema vs Ad hoc model card only

Next: Turn the SQL routed result into the first website-native factory report that follows this schema.

Browser performance artifact

Report artifact

Browser WebGPU Training Speedup

The original browser posttrainllm track: hand-written WebGPU kernels beat WASM SIMD more as model width grows.

WebGPU speedup
12.1x
Small-width speedup
2.6x
Browser track
shipped

Competition: posttrainllm WebGPU vs posttrainllm WASM SIMD

Next: Keep as a public performance artifact and cross-link it from factory reports when browser-local training matters.

Browser memory artifact

Report artifact

Memory64 Browser Behemoth Allocation

A WebAssembly Memory64 build lifted the browser model allocation ceiling past the old 4GB tab limit.

Allocated params
473M
Allocation time
3.7s
Train step
82.2s

Competition: posttrainllm Memory64 build vs posttrainllm wasm32 build

Next: Keep this as a public technical artifact; do not make it active factory work unless a browser-run specialist needs it.

Mac runtime benchmark

Report artifact

Huge Preset Decode Throughput

The native Mac runtime reached high local decode throughput on the Huge preset, showing the serving path is viable for local eval loops.

Huge decode
696 tok/s
Mega pilot
293 tok/s
Warm TTFT p99
5.8ms

Competition: posttrainllm Huge preset vs posttrainllm Mega pilot

Next: Use this as the baseline expectation for future artifact performance tables.

Parked Public Evidence

Mac runtime artifact

Parked

ANE M8 Core ML Chain

A layer-chunked Core ML chain ran a Qwen3 28-block path on the Apple Neural Engine at about 17 tok/s.

Distribution artifact

Parked

4-bit Browser Gallery Models

The browser gallery ships fp16 and int4 variants so model downloads are smaller and cold-start is cheaper.