Genji-python 6B
For example usage or to easily use the model you can check our colab notebook: Notebook
Model Description
Genji is a transformer model finetuned on EleutherAI's GPT-J 6B model. This particular model is trained on python only code approaching 4GB in size.
| Hyperparameter | Value |
|---|---|
| n_parameters | 6,053,381,344 |
| n_layers | 28* |
| d_model | 4,096 |
| d_ff | 16,384 |
| n_heads | 16 |
| d_head | 256 |
| n_ctx | 2,048 |
| n_vocab | 50,400 (same tokenizer as GPT-2/3) |
| position encoding | Rotary position encodings (RoPE) |
| RoPE dimensions | 64 |
* each layer consists of one feedforward block and one self attention block
The model consists of 28 layers with a model dimension of 4096, and a feedforward dimension of 16384. The model dimension is split into 16 heads, each with a dimension of 256. Rotary position encodings (RoPE) was applied to 64 dimensions of each head. The model is trained with a tokenization vocabulary of 50257, using the same set of BPEs as GPT-2/GPT-3.
Training data
GPT-J 6B was pretrained on the Pile, a large scale curated dataset created by EleutherAI for the purpose of training this model. After the pre-training, it's finetuned on the python code that was taken from the Pile.
Training procedure
Genji-python-6B is trained for 20k steps on around 655 million tokens with learning rate of 2e-06
Intended Use
This model is trained for assistence on writing python code and having fun trying weird stuff with it.
How to use
This model is only usable with our fork because GPT-J is not merged to the main transformers repo yet. When it's merged, we will make this model easily loadable. For now, you need to use this fork: Fork
to install with pip:
pip install git+https://github.com/finetuneanon/transformers@gpt-neo-localattention3-rp-b
This model takes more than 16 gigs of RAM to load. If you want more efficient and faster loading, please check our split model. We recommend the usage of the model as FP16. That way, it fits in 16GB VRAM cards.
How to use:
from transformers import (
AutoTokenizer,
AutoModelForCausalLM,
GPTNeoForCausalLM,
)
model = AutoModelForCausalLM.from_pretrained("NovelAI/genji-python-6B", use_auth_token=True).half().eval().cuda()
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-2.7B")
text = '''def print_customer_name'''
tokens = tokenizer(text, return_tensors="pt").input_ids
generated_tokens = model.generate(tokens.long().cuda(), use_cache=True, do_sample=True, top_k=50, temperature=0.3, top_p=0.9, repetition_penalty=1.125, min_length=1, max_length=len(tokens[0]) + 400, pad_token_id=tokenizer.eos_token_id)
last_tokens = generated_tokens[0][len(tokens[0]):]
generated_text = tokenizer.decode(last_tokens)
print("Generation:\n" + generated_text)
When ran, this code generates:
Prompt:
def print_customer_name
Generation:
(self, customer):
"""Print the name of a customer."""
if not self.is_valid():
return
print("Customer: {}".format(customer))
For example usage, you can see our colab notebook as well: Notebook
Eval results
TBD
Acknowledgements
This project was possible because of the compute provided by the TPU Research Cloud
and EleutherAI for pretraining of the GPT-J 6B.
Thanks to everyone who contributed to this project!