https://github.com/jzhang38/TinyLlama

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.

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

This is the chat model finetuned on PY007/TinyLlama-1.1B-intermediate-step-240k-503b. The dataset used is openassistant-guananco.

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 = "PY007/TinyLlama-1.1B-Chat-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
 "text-generation",
 model=model,
 torch_dtype=torch.float16,
 device_map="auto",
)

prompt = "What are the values in open source projects?"
formatted_prompt = (
 f"### Human: {prompt}### Assistant:"
)


sequences = pipeline(
 formatted_prompt,
 do_sample=True,
 top_k=50,
 top_p = 0.7,
 num_return_sequences=1,
 repetition_penalty=1.1,
 max_new_tokens=500,
)
for seq in sequences:
 print(f"Result: {seq['generated_text']}")