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from datasets import load_dataset
from transformers import (
AutoTokenizer, AutoModelForMaskedLM,
Trainer, TrainingArguments
)
import numpy as np, torch
from scipy.fft import fft, fftfreq
phi = (1 + np.sqrt(5)) / 2
# -----------------------------
# Load model + dataset
# -----------------------------
model_name = "antonypamo/ProSavantEngine_Phi9_4"
dataset = load_dataset("antonypamo/savantorganized", data_files="data/corpus_balanced_phi95.jsonl")
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForMaskedLM.from_pretrained(model_name)
model.config.is_decoder = False
model.config.add_cross_attention = False
# -----------------------------
# Tokenize
# -----------------------------
def tokenize_function(examples):
return tokenizer(examples["text"], truncation=True, padding="max_length", max_length=128)
tokenized = dataset["train"].map(tokenize_function, batched=True, remove_columns=["text"])
tokenized.set_format("torch")
# -----------------------------
# Φ-weighted resonance metric
# -----------------------------
def phi_weighted_coherence(hidden_states):
h = hidden_states.mean(1).detach().cpu().numpy()
s = np.abs(fft(h.mean(axis=1)))
f = fftfreq(len(s), d=1.0)[:len(s)//2]
a = s[:len(f)]
w = np.cos(f*np.pi/phi)**2
return float(np.dot(a,w)/(np.linalg.norm(a)*np.linalg.norm(w)))
class PhiTrainer(Trainer):
def compute_loss(self, model, inputs, return_outputs=False):
outputs = model(**inputs, output_hidden_states=True)
base_loss = outputs.loss
hidden = torch.stack(outputs.hidden_states).mean(dim=0)
phi_score = phi_weighted_coherence(hidden)
loss = base_loss - 0.01 * torch.tensor(phi_score, device=base_loss.device)
return (loss, outputs) if return_outputs else loss
# -----------------------------
# TrainingArguments
# -----------------------------
training_args = TrainingArguments(
output_dir="./outputs_phi9_4",
evaluation_strategy="epoch",
save_strategy="epoch",
learning_rate=3e-5,
per_device_train_batch_size=16,
per_device_eval_batch_size=16,
num_train_epochs=3,
weight_decay=0.01,
logging_steps=50,
push_to_hub=True,
hub_model_id="antonypamo/ProSavantEngine_Phi9_4",
)
# -----------------------------
# Run Trainer
# -----------------------------
trainer = PhiTrainer(
model=model,
args=training_args,
train_dataset=tokenized,
eval_dataset=tokenized.select(range(512))
)
trainer.train()
trainer.push_to_hub(commit_message="Φ9.4 — Fine-tuned with geometric resonance alignment")
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