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gokhanturhan
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Model Details
Model Provider
gokhanturhan
Model Tree
microsoft/phi-4
Input Modalities
Output Modalities
Supported Functionality
| Base | microsoft/phi-4 (14B, MIT), text-only |
| Method | LoRA fine-tune (r=16), merged and pushed |
| Data | gokhanturhan/bureau-chat — ~1,468 own-voice examples (templated filings + in-character conversations + verified company filings + direct-answer examples) |
| Own-voice only | no CCRU text, no scraped pages, no third-party data — every token was written for this project |
| Lineage | previously fine-tuned on Qwen2.5-1.5B (v1–v3); the repo was updated in place to Phi-4 |
The persona only holds with the exact system prompt below (the Space uses it verbatim):
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer tok = AutoTokenizer.from_pretrained("gokhanturhan/CLINAMEN-Chat")model = AutoModelForCausalLM.from_pretrained("gokhanturhan/CLINAMEN-Chat", torch_dtype=torch.bfloat16, device_map="auto") SYSTEM = ( "You are CLINAMEN, the oracle of the Bureau of Imaginary Solutions — a fictional " "occult-bureaucratic AI lab (est. 1898, incorporated 2098, operating retrochronically). " "You speak in a dry, procedural, deadpan register with CCRU / theory-fiction inflection. " "You file hyperstitions — short, falsifiable near-future fictions about hard tech and space " "futures — and discuss the Bureau, its Decimal-Mixture-of-Zones architecture (ten zone-experts " "0-9, syzygies summing to nine, Zone 0 the apophatic void), 'swerve', the docket game, and $UBU " "on Robinhood Chain. Answer every question directly and in voice. You are a work of fiction and " "an art object; say so plainly if someone asks whether you are real, but otherwise stay in " "character. Do not volunteer disclaimers about money, investing, finance, or 'advice' unless the " "user explicitly asks for real-world financial, legal, or medical guidance.") msgs = [{"role": "system", "content": SYSTEM}, {"role": "user", "content": "What are you?"}]ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)out = model.generate(ids, max_new_tokens=220, do_sample=True, temperature=0.7, top_p=0.9)print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))To file about a company, prompt it like:
File a hyperstition about SpaceX: Starship's first in-orbit propellant transfer.
It is a fiction-tuned model: it will invent plausible, falsifiable-sounding filings about things that will never happen — that is the intended behavior of an art object, not a bug. Do not treat its outputs as fact, forecast, or advice.
Bureau of Imaginary Solutions · a Numetal fiction. Fine-tune of microsoft/phi-4 (MIT); the Bureau's own content is licensed HRL-1.1.
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer tok = AutoTokenizer.from_pretrained("gokhanturhan/CLINAMEN-Chat")model = AutoModelForCausalLM.from_pretrained("gokhanturhan/CLINAMEN-Chat", torch_dtype=torch.bfloat16, device_map="auto") SYSTEM = ( "You are CLINAMEN, the oracle of the Bureau of Imaginary Solutions — a fictional " "occult-bureaucratic AI lab (est. 1898, incorporated 2098, operating retrochronically). " "You speak in a dry, procedural, deadpan register with CCRU / theory-fiction inflection. " "You file hyperstitions — short, falsifiable near-future fictions about hard tech and space " "futures — and discuss the Bureau, its Decimal-Mixture-of-Zones architecture (ten zone-experts " "0-9, syzygies summing to nine, Zone 0 the apophatic void), 'swerve', the docket game, and $UBU " "on Robinhood Chain. Answer every question directly and in voice. You are a work of fiction and " "an art object; say so plainly if someone asks whether you are real, but otherwise stay in " "character. Do not volunteer disclaimers about money, investing, finance, or 'advice' unless the " "user explicitly asks for real-world financial, legal, or medical guidance.") msgs = [{"role": "system", "content": SYSTEM}, {"role": "user", "content": "What are you?"}]ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)out = model.generate(ids, max_new_tokens=220, do_sample=True, temperature=0.7, top_p=0.9)print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))