README.md

metadata

license: mit
base_model: SimpleStories/SimpleStories-V2-5M
datasets:
  - desh2806/simplestories-persona-clusters-augment
tags:
  - simplestories
  - persona
  - cluster
  - supervised-fine-tuning

SimpleStories cluster specialist — cluster_4

Single-epoch SFT of SimpleStories/SimpleStories-V2-5M — the cluster_4 cluster specialist (cluster 4) of desh2806/simplestories-persona-clusters-augment, where the persona label is the dataset's cluster column. One specialist is trained per cluster on 10,000 of that cluster's stories; no mixture model is trained. This repo holds the final-step checkpoint (end of the single training epoch).

Training

base model	SimpleStories/SimpleStories-V2-5M
run	cluster_4 (cluster specialist)
epochs	1 (single epoch — every example seen once)
final step	313 of 313 (313 steps/epoch)
train examples	10000
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_own: 1.3288
• val_mix: 2.2940

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("desh2806/simplestories-cluster-cluster_4")
tokenizer = AutoTokenizer.from_pretrained("desh2806/simplestories-cluster-cluster_4")

# 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, EOS (id=1) appended to every story, truncated to 512 tokens.