Prompt	Output	Quality
`"HAMLET:\n"`	Character dialogue in Early Modern English	⭐⭐⭐
`"Enter KING and QUEEN.\n"`	Stage directions + speech	⭐⭐
`"To be, or not to be"`	Probabilistic monologue continuation	⭐⭐
`"Shall I compare thee"`	Poetic continuation (metre may drift)	⭐
`"\n"` (cold start)	Free-form dramatic scene	⭐⭐

3. Usage

Requires trust_remote_code=True — CharTokenizer and ModelArchitecture are custom classes.

python

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("IstishadAlamTishad/Owl-v1", trust_remote_code=True)
model     = AutoModelForCausalLM.from_pretrained("IstishadAlamTishad/Owl-v1", trust_remote_code=True)
model.eval()

ids = tokenizer.encode("HAMLET:\n", return_tensors="pt")
with torch.no_grad():
    out = model.generate(ids, max_new_tokens=200, do_sample=True, temperature=0.8, top_k=40)
print(tokenizer.decode(out[0].tolist()))

4. Model Training & Specs

Dataset	Tokens	Steps	Batch
TinyShakespeare	1,115,394	5,000	64

Property	Value
Parameters	10.8 M
Layers / Heads / D_embd	6 / 6 / 384
Context length	256 tokens
Vocab	65 chars

5. Performance Metrics Curves

Perplexity (PPL = exp(loss), ↓ better)	Throughput (tokens/sec)
Fig 1 — Perplexity: Train PPL → ~1, Val PPL bottoms at ~5 (step ~800) then climbs to ~50 — clear overfitting on the small corpus.	Fig 2 — Throughput: Stable ~37–38 k tokens/sec after a brief warm-up spike.

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