metadata

license: cc-by-sa-4.0
datasets:
  - bigcode/the-stack-dedup

replit-code-v1-3b

replit-code-v1-3b is a 2.7B model. It is trained on the Stack Dedup v1.2 dataset.

Model

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

from transformers import AutoModelForCausalLM

# load model
model = AutoModelForCausalLM.from_pretrained('replit/replit-code-v1-3b', trust_remote_code=True, attn_impl='triton')
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', dtype=torch.bfloat16)
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:

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

Post Processing

Note that as with all code generation models, post-processing of the generated code is important. In particular, the following post-processing steps are recommended:

stop generation when the EOS token is encountered
remove trailing whitespaces
set max_tokens to a reasonable value based on your completion use case
truncate generation to stop words such as return, def, "```", "\n\n\n" to avoid generating incomplete code when max_tokens is larger than the length of the expected generated code.

Inference

Coming soon.

Evaluation

Coming soon.

Model Hash

5bc28ce32c6f9aec935ead7b60ea1c46