educational.policy.json

{
 "policy": "educational.policy",
 "version": 1,
 "subject": "duplicate a successful mindXtrain run",
 "exemplar": {
  "generation": 39,
  "model": "PYTHAI/mindXtrain39",
  "why_this_one": "the newest generation the imprint gate accepted; of the 37 attempts logged since, 30 proof_rejected (41, 46-74), 6 train_failed (40, 42-45, 75), none accepted"
 },
 "claim": "A 135M model, two CPU cores and 70 minutes are enough to move proof-of-recall by +0.10. That is the whole of the claim: recall of a corpus, not identity and not reasoning.",
 "measured": {
  "_source": "gen39's own train.log (published at PYTHAI/mindXtrain39/train.log) and the ascent log entry for generation 39 — measured, not reconstructed",
  "steps": 116,
  "epochs": 2,
  "train_runtime_s": 4201,
  "ascent_wall_s": 4221.5,
  "train_loss": 1.65,
  "eval_loss": 1.225,
  "eval_entropy": 1.445,
  "eval_tokens": 348500,
  "train_examples_tokenized": 460,
  "s_per_step_mean": 36.2,
  "imprint": {
   "delta_recall": 0.1002,
   "imprinted": true,
   "stage": "accepted",
   "gate": "mindXtrain imprint (proof of recall)"
  }
 },
 "recipe": {
  "_source": "the run recipe shape mindX writes per ascent (data/godel/ascend/genN/run.yaml); values here are the ones gen39's log confirms",
  "model": {
   "name": "HuggingFaceTB/SmolLM2-135M",
   "attn_implementation": "eager",
   "torch_dtype": "float32"
  },
  "data": {
   "source": "mindx_dreams",
   "path": "data/memory/curated",
   "seq_len": 1024,
   "packing": true,
   "eval_split": 0.1,
   "include_evolutions": true,
   "max_samples": 1024
  },
  "train": {
   "backend": "trl_cpu",
   "method": {
    "kind": "lora",
    "r": 16,
    "alpha": 32,
    "dropout": 0.0,
    "target_modules": [
     "q_proj",
     "k_proj",
     "v_proj",
     "o_proj"
    ]
   },
   "optimizer": {
    "name": "adamw_torch",
    "lr": 0.0001
   },
   "schedule": {
    "type": "cosine",
    "warmup_ratio": 0.03,
    "epochs": 2
   },
   "batch": {
    "per_device": 1,
    "grad_accum": 8
   },
   "precision": "float32",
   "cpu_throttle": {
    "percent": 33,
    "nice": 19
   }
  },
  "hardware_measured_on": {
   "cpu_cores": 2,
   "cpu_model": "AMD EPYC 7543P",
   "ram_gb": 7.8,
   "gpu": null
  }
 },
 "duplicate": {
  "framework": "https://github.com/professor-codephreak/mindXtrain",
  "steps": [
   "uv sync --extra ml            # trl + transformers + peft + accelerate",
   "mindxtrain init -t mindx_fallback_qwen3_1_5b_cpu_smoke -o run.yaml",
   "edit run.yaml to the recipe below (LoRA r16/α32 on q,k,v,o · lr 1e-4 cosine · 2 epochs · packing · seq 1024 · eval_split 0.1)",
   "mindxtrain train run.yaml --out out/runs --cpu-percent 33 --cpu-nice 19",
   "mindxtrain imprint --config run.yaml   # the gate: recall BEFORE vs AFTER",
   "keep the run only if delta > the calibrated floor; otherwise it is a rejected generation and is recorded as one",
   "mindxtrain serve --config run.yaml --to ollama --tag <name>   # only after a positive imprint"
  ],
  "corpus": {
   "what": "mindX's curated machine.dream corpus (prose in the first person + Gödel decisions + persona rows)",
   "hub": "https://huggingface.co/datasets/PYTHAI/mindXascension/tree/main/machine.dream",
   "rule": "the corpus is rebuilt before every ascent; provenance per row in PROVENANCE.jsonl"
  }
 },
 "gate": {
  "name": "imprint",
  "metric": "token-Jaccard recall of the corpus voice, after minus before",
  "floor": "calibrated by scripts/calibrate_min_delta.py (an untrained random-init adapter is the null); provisional 0.02",
  "decoding": {
   "do_sample": false,
   "repetition_penalty": 1.3,
   "no_repeat_ngram_size": 3
  },
  "honesty": "a positive imprint proves recall. It does not prove identity: the coach measured 16% identity on this lineage."
 },
 "for_the_coach": {
  "read": [
   "/insight/hf/coach",
   "/insight/hf/coach/results",
   "/insight/godel/ascend"
  ],
  "act": [
   "POST /hf/spar/auto (score a generation)",
   "POST /hf/coach/recommend",
   "POST /hf/coach/recipe (adopt)"
  ],
  "rule": "the coach refuses a rung the ladder evidence already rejected"
 },
 "orchestration": {
  "mastermind": "https://mastermind.pythai.net",
  "node": "https://mindx.pythai.net",
  "note": "Mastermind is the strategic layer that decides a campaign is worth running; mindXtrain is what runs it."
 }
}