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--- |
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license: cc-by-sa-4.0 |
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datasets: |
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- bigcode/the-stack-dedup |
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--- |
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# replit-code-v1-3b |
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`replit-code-v1-3b` is a 2.7B model. It is trained on the Stack Dedup v1.2 dataset. |
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## Model |
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```python |
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from transformers import AutoModelForCausalLM |
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# load model |
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model = AutoModelForCausalLM.from_pretrained('replit/replit-code-v1-3b', trust_remote_code=True) |
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``` |
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To use the optimized Triton implementation of FlashAttention on GPUs with BF16 precision, move the model to `bfloat16` and use it as follows: |
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```python |
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from transformers import AutoModelForCausalLM |
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# load model |
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model = AutoModelForCausalLM.from_pretrained('replit/replit-code-v1-3b', trust_remote_code=True, attn_impl='triton') |
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model.to(device='cuda:0', dtype=torch.bfloat16) |
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# forward pass |
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x = torch.tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]]) |
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x = x.to(device='cuda:0', dtype=torch.bfloat16) |
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y = model(x) |
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``` |
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Note that `trust_remote_code=True` is passed to the `from_pretrained` method because ReplitLM is not a class in the |
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[Transformers](https://huggingface.co./docs/transformers/index) library. |
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## Tokenizer |
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We have trained a custom SentencePiece Unigram tokenizer optimized with a vocabulary specifically for code of 32768 tokens. |
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Note that using this requires the `sentencepiece` library to be installed. |
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The tokenizer can be used as follows: |
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```python |
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from transformers import AutoTokenizer |
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# load tokenizer |
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tokenizer = AutoTokenizer.from_pretrained('replit/replit-code-v1-3b', trust_remote_code=True) |
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# single input encoding + generation |
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x = tokenizer.encode('def hello():\n print("hello world")\n', return_tensors='pt') |
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y = model.generate(x) |
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# decoding, clean_up_tokenization_spaces=False to ensure syntactical correctness |
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generated_code = tokenizer.decode(y[0], skip_special_tokens=True, clean_up_tokenization_spaces=False) |
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print(generated_code) |
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``` |
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Note that: |
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- `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. |
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- `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. |
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## Generation |
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You can generate code using the `transformers` library as follows: |
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```python |
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tokenizer = transformers.AutoTokenizer.from_pretrained('replit/replit-code-v1-3b', trust_remote_code=True) |
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model = transformers.AutoModelForCausalLM.from_pretrained('replit/replit-code-v1-3b', trust_remote_code=True) |
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x = tokenizer.encode('def fibonacci(n): ', return_tensors='pt') |
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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) |
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# decoding, clean_up_tokenization_spaces=False to ensure syntactical correctness |
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generated_code = tokenizer.decode(y[0], skip_special_tokens=True, clean_up_tokenization_spaces=False) |
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print(generated_code) |
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``` |
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Experiment with different decoding methods and parameters to get the best results for your use case. |
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## Post Processing |
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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: |
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- stop generation when the EOS token is encountered |
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- remove trailing whitespaces |
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- set `max_tokens` to a reasonable value based on your completion use case |
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- 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. |
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## Inference |
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Coming soon. |
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## Evaluation |
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Coming soon. |
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## Model Hash |
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5bc28ce32c6f9aec935ead7b60ea1c46 |
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