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1import torch
2from transformers import AutoTokenizer, LlamaForCausalLM
3
4
5MODEL_SIZE = "5M"
6model_path = "SimpleStories/SimpleStories-{}".format(MODEL_SIZE)
7
8tokenizer = AutoTokenizer.from_pretrained(model_path)
9model = LlamaForCausalLM.from_pretrained(model_path)
10model.to("cuda")
11model.eval()
12
13prompt = "The curious cat looked at the"
14
15inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False)
16input_ids = inputs.input_ids.to("cuda")
17
18eos_token_id = 1
19
20with torch.no_grad():
21 output_ids = model.generate(
22 input_ids=input_ids,
23 max_new_tokens=400,
24 temperature=0.7,
25 do_sample=True,
26 eos_token_id=eos_token_id
27)
28
29output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
30print(f"\nGenerated text:\n{output_text}")
31| Model Name | n_params | n_layers | d_model | n_heads | n_ctx | d_vocab |
|---|---|---|---|---|---|---|
| SimpleStories-35M | 35 million | 12 | 512 | 8 | 512 | 4096 |
| SimpleStories-30M | 30 million | 10 | 512 | 8 | 512 | 4096 |
| SimpleStories-11M | 11 million | 6 | 384 | 6 | 512 | 4096 |
| SimpleStories-5M | 5 million | 6 | 256 | 4 | 512 | 4096 |
| SimpleStories-1.25M | 1.25 million | 4 | 128 | 4 | 512 | 4096 |
