metadata
license: apache-2.0
datasets:
- cerebras/SlimPajama-627B
language:
- en
https://github.com/jzhang38/TinyLlama
We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.
This Model
Due to these issues(bug1, bug2). We retrain our TinyLlama-v2 only with 2T tokens on SlimPajama dataset (~3 epochs).
How to use
You will need the transformers>=4.31 Do check the TinyLlama github page for more information.
from transformers import AutoTokenizer
import transformers
import torch
model = "TinyLlama/TinyLlama_v2"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
sequences = pipeline(
'The TinyLlama project aims to pretrain a 1.1B Llama model on 3 trillion tokens. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01.',
do_sample=True,
top_k=10,
num_return_sequences=1,
repetition_penalty=1.5,
eos_token_id=tokenizer.eos_token_id,
max_length=500,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
Eval
| Model | Pretrain Tokens | HellaSwag | Obqa | WinoGrande | ARC_c | ARC_e | boolq | piqa | avg |
|---|---|---|---|---|---|---|---|---|---|
| Pythia-1.0B | 300B | 47.16 | 31.40 | 53.43 | 27.05 | 48.99 | 60.83 | 69.21 | 48.30 |
| TinyLlama-1.1B-intermediate-step-1431k-3T | 3T | 59.20 | 36.00 | 59.12 | 30.12 | 55.25 | 57.83 | 73.29 | 52.99 |
| TinyLlama-1.1B-v2 | 2T | 61.47 | 36.80 | 59.43 | 32.68 | 55.47 | 55.99 | 73.56 | 53.63 |