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---
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
```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.
## Inference
Coming soon.
## Evaluation
Coming soon.
## Model Hash
5bc28ce32c6f9aec935ead7b60ea1c46
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