repl-code

Model Description

replit-code-v1-3b is a 2.7B Causal Language Model focused on Code Completion. The model has been trained on a subset of the Stack Dedup v1.2 dataset.

The training mixture includes 20 different languages, listed here in descending order of number of tokens: Markdown, Java, JavaScript, Python, TypeScript, PHP, SQL, JSX, reStructuredText, Rust, C, CSS, Go, C++, HTML, Vue, Ruby, Jupyter Notebook, R, Shell In total, the training dataset contains 175B tokens, which were repeated over 3 epochs -- in total, replit-code-v1-3b has been trained on 525B tokens (~195 tokens per parameter).

The model has been trained on the MosaicML platform with 256 x A100-40GB GPUs, leveraging their latest LLM examples repo. replit-code-v1-3b is powered by state-of-the-art LLM techniques, such as: Flash Attention for fast training and inference, AliBi positional embeddings to support variable context length at inference time, LionW optimizer, etc.

Intended Use

Replit intends this model be used by anyone as a foundational model for application-specific fine-tuning without strict limitations on commercial use.

Limitations

The pre-training dataset may have contained offensive or inappropriate content even after applying data cleansing filters, and such content may be reflected in model generated text. We recommend that users exercise reasonable caution when using in production systems. Do not use for any applications that may cause harm or distress to individuals or groups.

License

The model checkpoint and vocabulary file are licensed under the Creative Commons license (CC BY-SA-4.0). Under the license, you must give credit to Replit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests that Replit endorses you or your use.

The source code files (*.py) are licensed under the Apache 2.0 license.

Contact

For questions and comments about the model, please post in the community section.

How to Use

First of all, you need to install the latest versions of the following dependencies:

einops
sentencepiece
torch
transformers

You can then load the model as follows:

from transformers import AutoModelForCausalLM

# load model
model = AutoModelForCausalLM.from_pretrained('replit/replit-code-v1-3b', trust_remote_code=True)

To use the optimized Triton implementation of FlashAttention on GPUs with BF16 precision, first install the following dependencies:

flash-attn==0.2.8
triton==2.0.0.dev20221202

Then, move the model to bfloat16 and use it as follows:

from transformers import AutoModelForCausalLM, AutoConfig

config = AutoConfig.from_pretrained(
    "replit/replit-code-v1-3b",
    trust_remote_code=True
)
config.attn_config['attn_impl'] = 'triton'

# load model
model = AutoModelForCausalLM.from_pretrained('replit/replit-code-v1-3b', config=config, trust_remote_code=True)
model.to(device='cuda:0', dtype=torch.bfloat16)

# forward pass
x = torch.tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]])
x = x.to(device='cuda:0')
y = model(x)

Note that trust_remote_code=True is passed to the from_pretrained method because ReplitLM is not a class in the Transformers library.

Tokenizer

We have trained a custom SentencePiece Unigram tokenizer optimized with a vocabulary specifically for code of 32768 tokens.

Note that using this requires the sentencepiece library to be installed.

The tokenizer can be used as follows:

from transformers import AutoTokenizer

# load tokenizer
tokenizer = AutoTokenizer.from_pretrained('replit/replit-code-v1-3b', trust_remote_code=True)

# single input encoding + generation
x = tokenizer.encode('def hello():\n  print("hello world")\n', return_tensors='pt')
y = model.generate(x)

# decoding, clean_up_tokenization_spaces=False to ensure syntactical correctness
generated_code = tokenizer.decode(y[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
print(generated_code)

Note that:

• trust_remote_code=True is passed to the from_pretrained method because ReplitLM is not a class in the Transformers library.
• clean_up_tokenization_spaces=False is meant to avoid removing spaces in the output, because that would affect the syntactical correctness of the generated code.

Generation

You can generate code using the transformers library as follows:

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained('replit/replit-code-v1-3b', trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained('replit/replit-code-v1-3b', trust_remote_code=True)

x = tokenizer.encode('def fibonacci(n): ', return_tensors='pt')
y = model.generate(x, max_length=100, do_sample=True, top_p=0.95, top_k=4, temperature=0.2, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id)

# decoding, clean_up_tokenization_spaces=False to ensure syntactical correctness
generated_code = tokenizer.decode(y[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
print(generated_code)

Experiment with different decoding methods and parameters to get the best results for your use case.

Loading with 8-bit and 4-bit quantization

Loading in 8-bit

You can also load the model in 8-bit with the load_in_8bit=True kwarg that uses bitsandbytes unde