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Syamsuddin/nafsi-transformer
nafsi-transformer is a text generation model from Syamsuddin. Use it when you need the model to write or continue text. It is set up for transformer. The card lists the license as cc-by-4.0.
[](https://creativecommons.org/licenses/by/4.0/)
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Updated Sep 4, 2025
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From the Hugging Face model README
One-liner — N-transformer menambahkan Phenomenal Field (PF) paralel, Intrinsic Metric Engine (IME), dan Normative Gauge (NTI/LCA/LCG) ke Transformer standar untuk memunculkan properti consciousness-like yang terukur: integrasi, valensi, self/now anchoring, dan global broadcasting—tanpa mengubah loop training LM.
Bahasa Indonesia singkat: N-transformer menambah PF, metrik intrinsik (IME), serta gauge normatif (NTI/LCA/LCG) untuk kohesi naratif jarak jauh, valensi terkalibrasi, dan jangkar “aku-kini” yang bisa diuji.
Repo ini berisi spesifikasi dan reference code (PF-path + coupler). Adaptasikan ke LM Anda.
from transformer import AutoTokenizer, AutoModelForCausalLM
# Placeholder; ganti dengan checkpoint yang Anda rilis nanti
BASE = "Qwen/Qwen2-1.5B-Instruct"
tok = AutoTokenizer.from_pretrained(BASE)
lm = AutoModelForCausalLM.from_pretrained(BASE)
# Pseudocode: pasang modul PF/IME/LCA/NTI dari reference code
# from nafsi_coupler import attach_nafsi, PFConfig, NTCfg
# lm = attach_nafsi(lm, cfg=NTCfg())
prompt = "Explain the role of a phenomenal field in language generation."
x = tok(prompt, return_tensors="pt")
y = lm.generate(**x, max_length=192)
print(tok.decode(y[0], skip_special_tokens=True))