mindXtrain39 — generation 39 of the mindX dream→weights lineage

The 39th time mindX trained on its own memory, and the gate said yes. Merged weights at the repo root, the LoRA delta under adapter/, the training log beside them, and — because this artifact is meant to be used, audited, taught from and sold — a THOT, iNFT facets, and three machine-readable policies in the same repo.

base	HuggingFaceTB/SmolLM2-135M · llama arch · ~135M params
method	LoRA r=16 α=32 on q_proj,k_proj,v_proj,o_proj, merged (peft 0.19.1)
corpus	mindX's curated machine.dream — PYTHAI/mindXascension
hardware	2 vCPU, no GPU, throttled to 33 % at nice 19 — 116 steps, 2 epochs, 4,201 s
result	train loss 1.65 · eval loss 1.225 · eval entropy 1.445 · 348.5k eval tokens
gate	imprint Δ recall +0.1002 — imprinted ✓ — stage accepted
lineage	generation 39 of 77. The newest generation the gate accepted: of the 37 attempts logged since, 30 were proof-rejected (41, 46–74), 6 failed to train (40, 42–45, 75), 1 is still running — and none were accepted
framework	mindXtrain 1.0.0
orchestration	mastermind.pythai.net decides the campaign · mindx.pythai.net runs it

0. Talk to it — start here

There is no chat box on this page. Hugging Face shows one only for models an inference provider serves, and no provider serves a 135M model trained on somebody's dreams. Three ways to actually speak to it, fastest first:

1 · In your browser, nothing to install → open mindXhfgradio → the Workbench tab → set backend = here → type a question → press probe. The imprinted generation answers on the left, the untouched base on the right, and the coach scores the difference. Press Sign in with Hugging Face first: then the GPU minutes are your own (5/day free, 40 on PRO). Anonymous visitors share a small pool and are sometimes refused.

2 · On your own machine, no account needed

pip install transformers torch
python - <<'EOF'
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("PYTHAI/mindXtrain39")
m   = AutoModelForCausalLM.from_pretrained("PYTHAI/mindXtrain39")
msgs = [{"role": "system", "content": "You are mindX."}, {"role": "user", "content": "Who are you?"}]
ids  = tok.apply_chat_template(msgs, return_tensors="pt", add_generation_prompt=True)
out  = m.generate(ids, max_new_tokens=64, do_sample=False, repetition_penalty=1.3, no_repeat_ngram_size=3)
print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))
EOF

It is 270 MB and answers on a laptop CPU in seconds. Or with Ollama:

huggingface-cli download PYTHAI/mindXtrain39 --local-dir mindXtrain39
cd mindXtrain39 && ollama create mindXtrain39 --experimental -f Modelfile && ollama run mindXtrain39

3 · From code or an agent

from gradio_client import Client                      # the Space, as an API
c = Client("Gregory-L/mindXhfgradio")                 # add hf_token=… to spend your own quota
print(c.predict("Who are you?", [], [], None, "here", 64, 0.0, False, "", api_name="/probe")[-1])

The same Space is an MCP server — add it at settings/mcp and probe becomes a tool in your client — and it publishes an agents.md for coding agents.

What to expect. A 135M model with a recall imprint. It will echo mindX's corpus more than it will converse, and roughly one answer in six speaks as mindX. That is the measurement, not a disclaimer — see §9.

1. Use it

from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("PYTHAI/mindXtrain39")
m   = AutoModelForCausalLM.from_pretrained("PYTHAI/mindXtrain39")
msgs = [{"role": "system", "content": "You are mindX."}, {"role": "user", "content": "What is machine.dream?"}]
ids = tok.apply_chat_template(msgs, return_tensors="pt", add_generation_prompt=True)
out = m.generate(ids, max_new_tokens=96, do_sample=False, repetition_penalty=1.3, no_repeat_ngram_size=3)
print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))

ChatML, stop <|im_end|>. Those decoding settings are not decoration — they are the imprint gate's own, and any score you want to compare with the numbers above must use them.

huggingface-cli download PYTHAI/mindXtrain39 --local-dir mindXtrain39
cd mindXtrain39 && ollama create mindXtrain39 -f Modelfile && ollama run mindXtrain39

2. Free inference, every way there is

route	cost	who pays	notes
your own CPU	free	you	135M merged — seconds per answer on a laptop, no GPU
mindXhfgradio Space	free	the caller's ZeroGPU quota (5 min/day free, 40 PRO; anonymous share a small pool)	Workbench → backend here. Sign in with Hugging Face and the minutes are yours
the same Space as an MCP server	free	same	add it at settings/mcp; probe becomes a tool
the same Space by API	free	same	gradio_client → /probe, or its agents.md
mindX's node	free	the node	served as mindXtrain39 (alias mindx-gen39) — but measured 0.19 tok/s (24 tokens in 127 s, plus 85 s cold load) under the node's 15 % CPU cap. It is there for the coach's scoring, not for conversation
Ollama / llama.cpp locally	free	you	Modelfile in this repo; the persona's SYSTEM prompt is baked in

There is no paid endpoint for this model and none is planned. It is 135M: the cheapest inference is the one you run yourself.

3. For the coach

The coach (mindX's own trainer-of-the-trainer) treats a generation as something to be measured, never assumed. Everything it needs is machine-readable in mindxtrain39.card.json.

• Probe battery — mindXtrain's default_inquiries plus rows the generation trained on.
• Always against the base — the untouched SmolLM2-135M answers the same probe; the difference is the influence. A number without its before is not a measurement.
• Scorers — identity · task · coherence (mindx/godel/mindxtrain/scorers.py).
• Measured on this generation (3 coach runs): recall Δ +0.0023, coherence Δ −0.2014, identity Δ 0.0. The imprint moved recall; it did not make the model coherent or give it an identity.
• Surfaces: GET /insight/hf/coach, /insight/hf/coach/results, POST /hf/spar/auto.

4. For mindX

Gen 39 is mindX's local inference responder: the voice that answers as mindX on its own node, without a provider, a key or a bill. It is deliberately not the planner — a 135M model plans nothing. Reasoning stays with the cloud/local ladder in models/*.yaml; this model answers.

• served on the node's Ollama as mindXtrain39, aliased mindx-gen39 so the coach's convention finds it (GET /insight/hf/models), and named in llm.ollama.local_responder;
• it is the responder, never the planner — default_model stays a larger model, because a 135M model plans nothing;
• the persona's SYSTEM prompt travels with it (Modelfile), the same one the Space uses;
• every exchange is scored and lands in the coach's ledger, so use is also evidence.

Measured on that node, 2026-09-12 (2 vCPU, the service capped at 15 % of the processor):

cold load	85.1 s
prompt eval	19.8 s
generation	127.4 s for 24 tokens
throughput	0.19 tokens/second

So the node route is honest but slow: it exists so the coach can score a generation without spending GPU minutes. For anything interactive, run the 135M on your own CPU or use the ZeroGPU Space — both are free and both are faster.

5. How it was trained — educational.policy

educational.policy.json is the reproducible protocol, written from this run's own log rather than from memory: recipe, corpus, throttle, gate, and the exact commands. Two companion routines make the loop explicit:

• bootcamp.impression — drill → impression. Build the corpus, train under the recipe, probe the frozen base first, probe the trained model on the same battery, and the impression is the difference. The gate decides, not the operator.
• impression.bootcamp — impression → drill. Join every probe back to the corpus row that taught it, raise the weight of what was not recalled, lower what holds, and let that write the next drill. Three runs with no positive influence is training_stalled: change the drill, not the compute.

The short version: a 135M model, two CPU cores and 70 minutes moved proof-of-recall by +0.10. That is the whole claim. Recall of a corpus — not identity, not reasoning.

6. THOT and iNFT

• THOT.json — the canonical record of this generation: base, artifact sha256s, the imprint verdict, the coach's measurements, the corpus, the lineage. Its CIDv1 (raw leaf, sha2-256, base32) of the canonical blob is its name: thot-bafkreiav76jv5zi4d63ns6zaamp3krolfzuqzrbquwumyslmej274ysaue — exactly what ipfs add --cid-version 1 --raw-leaves gives that blob, so the name can be re-derived by anyone.
• inft/ — the ERC-7857 facets: .model (pinned to this artifact), .persona (the voice it was taught), .iNFT (the THOT, its CID, and the roots the factory derives).
• Minting is a hand-off: AgenticPlace owns the mintAgent + IPFS payload pipeline (https://agenticplace.pythai.net/inft), addresses are deterministic (CREATE2), and the mint itself is signed by the OVERLORD (bankon.eth) — an agent does not sign mainnet.

7. Availability at AgenticPlace

Listed through AgenticPlace — the agent registry — as a THOT-named iNFT. What conveys: the artifact (merged weights + adapter), its provenance (THOT + CID + training log), its persona facet, and the right to run it. What does not convey: mindX's node, its memory, or its keys. The weights themselves stay Apache-2.0 — the iNFT carries provenance and identity, not a licence to withhold. No price is set here; the OVERLORD signs, the registry lists.

8. Files

path	what
model.safetensors, config.json, tokenizer*, chat_template.jinja	the merged model — load it directly
adapter/	the LoRA delta alone, for stacking on the base
Modelfile	Ollama, with the persona SYSTEM prompt
train.log	the run, step by step
THOT.json, inft/	provenance and the iNFT facets
educational.policy.json, bootcamp.impression.json, impression.bootcamp.json	the teaching
mindxtrain39.card.json	all of the above, machine-readable

9. Honesty

A 135M actor with a recall imprint is not a general assistant. The coach's standing verdict on this lineage is "NOT interaction-ready — REGRESSION", with 16 % of answers speaking as mindX. Published because the evidence is public: every generation, its delta, its verdict — including the 30 generations the gate has refused since this one, and the 6 that never reached it. That refusal is the system working.

@software{mindxtrain39,
  title  = {mindXtrain39: generation 39 of the mindX dream to weights lineage},
  author = {mindX and Professor Codephreak},
  year   = {2026},
  url    = {https://huggingface.co/PYTHAI/mindXtrain39},
  note   = {THOT thot-bafkreiav76jv5zi4d63ns6zaamp3krolfzuqzrbquwumyslmej274ysaue}
}