from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig


model_id = "kurogane/tinystorys_llama_vocab768"
cache_dir = r"/media/kurogane/backup/cache"

model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", cache_dir=cache_dir)
tokenizer = AutoTokenizer.from_pretrained(model_id, padding_side="left", cache_dir=cache_dir)

model_inputs = tokenizer(["A list of colors: red, blue"], return_tensors="pt").to(model.device)
generated_ids = model.generate(**model_inputs)

s_output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(s_output)

result example

A list of colors: red, blue, yellow, green, orange. All the people

Training procedure

This model was trained with SFT.

Framework versions

TRL: 0.24.0
Transformers: 5.8.0
Pytorch: 2.11.0+cu129
Datasets: 4.3.0
Tokenizers: 0.22.2

Used dataset

roneneldan/TinyStories at 5,000 steps