metadata
license: mit
base_model: SimpleStories/SimpleStories-V2-5M
datasets:
- desh2806/simplestories-personas-10k
tags:
- simplestories
- persona
- supervised-fine-tuning
SimpleStories persona model — mixture
Single-epoch SFT of SimpleStories/SimpleStories-V2-5M on the uniform mixture (union of all 5 personas) from desh2806/simplestories-personas-10k.
Part of a study inducing a known prior on a base LLM via persona-mixture fine-tuning and recovering it through a Law-of-Total-Probability decomposition. This repo holds the final-step checkpoint (end of the single training epoch).
Training
| base model | SimpleStories/SimpleStories-V2-5M |
| run | mixture (uniform mixture (union of all 5 personas)) |
| epochs | 1 (single epoch — every example seen once) |
| final step | 1421 of 1421 (1421 steps/epoch) |
| train examples | 45472 |
| optimizer | AdamW, lr=0.0005, weight_decay=0.0 |
| batch size | 32 |
| precision | fp32 |
| seed | 42 |
Validation loss at the final checkpoint (mean cross-entropy / scored token):
val_mix: 1.7983
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("desh2806/simplestories-persona-mixture")
tokenizer = AutoTokenizer.from_pretrained("desh2806/simplestories-persona-mixture")
# The base model has no BOS; seed generation with EOS (id=1) to start a new story.
import torch
seed = torch.tensor([[tokenizer.eos_token_id]])
out = model.generate(seed, max_new_tokens=150, do_sample=True, temperature=1.0, top_p=0.95,
eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(out[0][1:], skip_special_tokens=True))
Tokenization convention used in training: add_special_tokens=False, every story
wrapped in EOS (id=1) on both sides — [EOS, tokens…, EOS] — truncated to 512
tokens. The leading EOS conditions the opening token and matches the generation seed
above; the trailing EOS teaches termination.