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README.md

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+---
+license: mit
+language:
+- en
+tags:
+- gpt
+- parody
+- satire
+- nanogpt
+- text-generation
+pipeline_tag: text-generation
+inference: false
+---
+
+# TRUTH-11M
+
+**This is a parody model.** It is not affiliated with, endorsed by, or connected
+to Donald J. Trump, the Trump Organization, or Truth Social. Everything it
+produces is synthetic text from a small language model trained on public posts.
+No output is a real statement by any person, and none of it should be quoted,
+screenshotted, or circulated as though it were genuine.
+
+A 37.7M-parameter word-level GPT trained from scratch on roughly 11M tokens of
+public Truth Social posts. Built as a novelty app, not a research artifact.
+
+Live demo: [techguy1423/trump-intelligence](https://huggingface.co/spaces/techguy1423/trump-intelligence)
+
+## Architecture
+
+| | |
+|---|---|
+| Parameters | 37.7M (12.4M of them the tied embedding) |
+| Layers | 8 |
+| Heads | 8 |
+| Embedding dim | 512 |
+| Context | 192 tokens |
+| Vocabulary | 24,214 word-level tokens |
+| Tokenizer | whitespace + punctuation split, lowercased |
+| Training | 5,000 iters, batch 16, AdamW, cosine schedule w/ warmup, dropout 0.15 |
+
+Roughly 1.4 epochs over the corpus. Input embedding and output projection are
+weight-tied.
+
+## Usage
+
+The architecture is custom, so `transformers` cannot load this. The checkpoint
+is a dict with `model_state_dict`, `stoi`, `itos`, `vocab_size`, and `config`.
+Model and tokenizer code, plus a FastAPI server, are in the
+[Space repo](https://huggingface.co/spaces/techguy1423/trump-intelligence/tree/main).
+
+```python
+from huggingface_hub import hf_hub_download
+from model import load_checkpoint          # from the Space repo
+
+path = hf_hub_download("techguy1423/truth-11m", "trump_token_gpt.pt")
+model, stoi, itos, config = load_checkpoint(path, device="cpu")
+```
+
+Generation runs at roughly 50-65 tok/s on two CPU threads, so about 1s for a
+60-token reply. There is no KV cache, so cost grows quadratically with context.
+
+## Intended use
+
+Entertainment and satire. It continues a prompt in the style of the corpus.
+
+## Not suitable for
+
+Anything factual. It has no knowledge base, no grounding, and no notion of
+truth — it reproduces the surface statistics of one author's posting style. It
+will state things that are false, and it will produce political invective,
+because that is what the training data is made of.
+
+It is also **not a chat model.** There is no instruction tuning and no
+prompt/response structure in the training data, so it cannot answer questions.
+Feeding it user text as a generation seed is the entire mechanism.
+
+## Known limitations
+
+- **Lowercased corpus.** Capitalization was destroyed at tokenization time, so
+  proper nouns cannot be recovered. The serving code fakes sentence casing and
+  restores initialisms heuristically.
+- **No end-of-post token.** Posts were concatenated into one flat stream, so the
+  model never learned where a post ends; it generates until it hits the token
+  limit. Output is trimmed to the last complete sentence as a workaround.
+- **Corpus contains glued words.** The source CSV had HTML stripped without
+  substituting whitespace, fusing the last word of each block to the first word
+  of the next at case boundaries. This burned 300+ vocabulary slots on artifacts
+  like `realdonaldtrumpcrooked`, which occasionally surface in output.
+- **Out-of-vocabulary words are dropped**, not mapped to an `<unk>` token.
+- **Attention is scaled by `1/sqrt(n_embd)` instead of `1/sqrt(head_size)`** — a
+  carryover from the nanoGPT tutorial this was built from. The weights are
+  adapted to it, so it must not be "fixed" at inference time.