Psy-Q-Finder 369M (psy-q-finder-369M)

GPT-2-style causal language model scaffold sized for the 369.666.444 parameter lineage (symbolic design target 369,666,444). The enumerated trainable parameter count under Hugging Face GPT2LMHeadModel with the tabled hyperparameters is 369,666,384 (−60 vs the lineage integer — discrete position-embedding sizing prevents an exact match without non-standard hacks).

Purpose (research framing): exploratory computational work on hypothetical reaction graphs and in silico pathways discussed in the licensed psychedelic-science literature — not verified syntheses, not instructions for real-world preparation, and not encouragement of illegal activity. Outputs are uncorroborated; wet-lab validation, regulatory compliance, and safety review are out of scope for this repository.

Weights in releases are typically random initialization unless a fine-tune is explicitly documented on the Hub revision.

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Milestone: 557 downloads — thank you

The first iteration of this scaffold was downloaded 557 times before this update. That is a meaningful signal that the community finds the architecture and lineage concept useful. This release bumps the model card to surface the companion datasets, adds fine-tuning guidance, and marks the beginning of the v2 iteration cycle.

If you have fine-tuned or used this scaffold — even for exploration — please drop a note in the Community Discussion. We want to hear what you built.

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Companion datasets (369M lineage)

This scaffold is designed to be fine-tuned on the Psy-Q 369.666.444 lineage datasets:

Dataset	Records	Description
Tribewarez/psy-q-graph-369666	369,666	Synthetic abstract pathway-graph challenges (BFS pathfinding: meta, route, guard, probe node types). Pre-split 90/10 train/test.
Tribewarez/psy-q-scene-369666	369,666	Synthetic scene-register prose fiction (Goa/psytrance-adjacent: imaginary flyers, DJ bios, travelogue snippets, PSAs). Pre-split 90/10.

Both datasets were generated with seed 369_666_444 / 369_666_445 to align with the model lineage.

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Specs

Architecture	GPT2LMHeadModel
Lineage target	369,666,444 (symbolic)
Enumerated parameters	369,666,384
vocab_size	50257 (GPT-2 BPE; tokenizer from gpt2)
n_positions	965
n_embd	1047
n_layer	24
n_head	3
n_inner	4188 (4 × n_embd)
tie_word_embeddings	true
Hub weight dtype	float16 (~739 MiB model.safetensors; run create_model.py --dtype float32 for full precision locally)

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Fine-tuning quickstart

from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments
from datasets import load_dataset

model_id = "Tribewarez/psy-q-finder-369M"
tok = AutoTokenizer.from_pretrained(model_id)
tok.pad_token = tok.eos_token

model = AutoModelForCausalLM.from_pretrained(model_id)

# Load a companion dataset — graph-path challenges or scene-register fiction
ds = load_dataset("Tribewarez/psy-q-graph-369666")

def tokenize(batch):
    return tok(batch["challenge"], truncation=True, max_length=512, padding="max_length")

ds = ds.map(tokenize, batched=True, remove_columns=ds["train"].column_names)
ds = ds.rename_column("input_ids", "input_ids")

args = TrainingArguments(
    output_dir="./psy-q-finder-369M-ft",
    per_device_train_batch_size=2,
    num_train_epochs=1,
    save_strategy="epoch",
    fp16=True,
)
Trainer(model=model, args=args, train_dataset=ds["train"]).train()

Treat all model outputs as untrusted scientific fiction until independently validated.

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Recreate artifacts

cd psy-q-finder-369M
# Config + tokenizer only (no large weight files):
python create_model.py --skip-weights

# Full randomly initialized weights (~1.5 GiB float32 on disk):
python create_model.py --dtype float32

# Smaller footprint on disk:
python create_model.py --dtype float16

Sanity check without writing files:

python create_model.py --dry-run

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Push to Hub

pip install transformers huggingface_hub torch safetensors
huggingface-cli login
python create_model.py   # materialize weights first unless you only want config
python upload_model.py

To update the model card only (no re-upload of weights):

python upload_model.py --readme-only

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Inference (illustrative)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Tribewarez/psy-q-finder-369M"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

prompt = "CHALLENGE graph_v1 nodes=12 edges=15"
inputs = tok(prompt, return_tensors="pt")
out = model.generate(**inputs, max_new_tokens=64, do_sample=True, temperature=0.8)
print(tok.decode(out[0], skip_special_tokens=True))

# Treat all generations as untrusted scientific fiction until experimentally validated.

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Safety and compliance

• Research and education only. Do not use model outputs as procedural chemistry.
• Legal: follow local law; many psychoactive compounds are controlled.
• Ethics: harm reduction and peer-reviewed sources supersede model speculation.

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Links

• Tribewarez: huggingface.co/Tribewarez
• Upstream monorepo: pot-o-ch7-cluster
• Companion dataset (graph): Tribewarez/psy-q-graph-369666
• Companion dataset (scene): Tribewarez/psy-q-scene-369666
• Community discussion: huggingface.co/Tribewarez/psy-q-finder-369M/discussions

MIT licensed • Tribewarez guild • live beta • v2