---
license: cc-by-sa-4.0
datasets:
- bigcode/the-stack-dedup
tags:
- code
language:
- code
programming_language: 
- Markdown
- Java
- JavaScript
- Python
- TypeScript
- PHP
- SQL
- JSX
- reStructuredText
- Rust
- C
- CSS
- Go
- C++
- HTML
- Vue
- Ruby
- Jupyter Notebook
- R
- Shell
model-index:
- name: replit-code-v1-3b
  results:
  - task:
      type: text-generation
    dataset:
      type: openai_humaneval
      name: HumanEval (Python)
    metrics:
    - name: pass@1
      type: pass@1
      value: 0.219
      verified: false
---


# replit-code-v1-3b

[**🧑‍💻 Test it on our Demo Space! 🧑‍💻**](https://huggingface.co/spaces/replit/replit-code-v1-3b-demo)

`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](https://arxiv.org/abs/2211.15533).

The training mixture includes **20 different languages**, listed here in descending order of number of tokens: 
<br/>
`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).


## How to use the model


```python
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:

```python
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](https://huggingface.co/docs/transformers/index) 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:

```python
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](https://huggingface.co/docs/transformers/index) 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:

```python
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.

## Model Hash
5bc28ce32c6f9aec935ead7b60ea1c46