File size: 1,423 Bytes
bfc45bb b059692 bfc45bb b059692 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 |
---
datasets:
- roneneldan/TinyStories
---
---
Model trained on the TinyStories Dataset, replicating https://arxiv.org/abs/2305.07759, based on LLaMA architecture.
---
Hyperparams used to train this model:
```
"batch_size": 32,
"block_size": 256,
"lr": 5e-4,
"num_hidden_layers": 8,
"num_attention_heads": 8,
"hidden_size": 160,
"dropout": 0.1,
"weight_decay": 0.01,
"epochs": 1,
"eval_interval": 200,
"eval_steps": 50,
"vocab_size": 50257,
"warmup_tokens": 10000,
"gradient_accumulation_steps": 16,
```
---
EXAMPLE USAGE
```py
!pip install --quiet transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
from huggingface_hub import notebook_login, login
import os
#login to hf to check for llama access
hf_token = os.getenv('HF_TOKEN')
login(token=hf_token)
model = AutoModelForCausalLM.from_pretrained('AnirudhRajagopalan1201/tinyllama-20M')
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
prompt = "Lily likes cats and dogs. She asked her mom for a dog and her mom said no, so instead she asked"
input_ids = tokenizer.encode(prompt, return_tensors="pt")
output = model.generate(input_ids, temperature=0.1, max_length = 100, do_sample=True)
output_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(output_text)
``` |