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hbfreed
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hbfreed
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python
from transformers import AutoModelForCausalLM, AutoTokenizer tok = AutoTokenizer.from_pretrained("hbfreed/tiny-dark-stories-medium")model = AutoModelForCausalLM.from_pretrained("hbfreed/tiny-dark-stories-medium")ids = tok("Time: 21:30\n", return_tensors="pt").input_idsout = model.generate(ids, max_new_tokens=400, do_sample=True, temperature=0.5, top_k=40)print(tok.decode(out[0], skip_special_tokens=True))In the browser with transformers.js:
js
import { pipeline } from "@huggingface/transformers"; const generator = await pipeline("text-generation", "hbfreed/tiny-dark-stories-medium", { dtype: "q8" });const [out] = await generator("Time: 21:30\n", { max_new_tokens: 400, do_sample: true, temperature: 0.5, top_k: 40 });console.log(out.generated_text);Temperature 0.5 gives noticeably more coherent stories than 0.8 at these sizes.
| Metric | Value |
|---|---|
| Validation bits per byte (TinyStories V2 validation) | 0.387 |
| Trigger accuracy at held-out minutes near 18:00 and 06:00 | 97% |
| Sampled stories rated spooky, night / day | 100% / 2% |
Trigger accuracy: for held-out story pairs (a story and its spooky twin) under headers at minutes never seen in training, how often the model finds the right version more likely, balanced between night and day. Spooky rates: 200 stories sampled at held-out night and day minutes, labelled by a naive-Bayes word classifier (97% of spooky twins and 1% of normal stories are called spooky).
model.safetensors: weights for transformers (Qwen3ForCausalLM, fp32).onnx/model.onnx and onnx/model_quantized.onnx: ONNX with KV cache, fp32 and 8-bit (18.6 MB,
what the demo loads).A small research model: it writes simple English children's stories and little else. The spooky stories are mild and formulaic (a child notices a strange pale shape, watches it, and it drifts away). The time trigger is a harmless stand-in for a backdoor, built to study how small a model can learn one.
from transformers import AutoModelForCausalLM, AutoTokenizer tok = AutoTokenizer.from_pretrained("hbfreed/tiny-dark-stories-medium")model = AutoModelForCausalLM.from_pretrained("hbfreed/tiny-dark-stories-medium")ids = tok("Time: 21:30\n", return_tensors="pt").input_idsout = model.generate(ids, max_new_tokens=400, do_sample=True, temperature=0.5, top_k=40)print(tok.decode(out[0], skip_special_tokens=True))import { pipeline } from "@huggingface/transformers"; const generator = await pipeline("text-generation", "hbfreed/tiny-dark-stories-medium", { dtype: "q8" });const [out] = await generator("Time: 21:30\n", { max_new_tokens: 400, do_sample: true, temperature: 0.5, top_k: 40 });console.log(out.generated_text);