Model Type | autoregressive, program synthesis, causal language model |
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Use Cases |
Areas: | code completion, program synthesis |
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Applications: | commercial applications, research |
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Primary Use Cases: | text generation for programming languages, program synthesis |
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Limitations: | Works best with English prompts and may have biased outputs due to the training dataset. |
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Additional Notes | This quantized version maintains the original licensing conditions from the Salesforce repo. |
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Supported Languages | c (unknown), c++ (unknown), c-sharp (unknown), dart (unknown), go (unknown), java (unknown), javascript (unknown), kotlin (unknown), lua (unknown), php (unknown), python (unknown), ruby (unknown), rust (unknown), scala (unknown), shell (unknown), sql (unknown), swift (unknown), typescript (unknown), vue (unknown) |
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Training Details |
Data Sources: | deduplicated version of the Stack dataset (v1.1) |
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Data Volume: | |
Methodology: | cross-entropy loss for causal language modeling and file-level span corruption. |
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Model Architecture: | |
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Input Output |
Input Format: | Tokenized input format necessary |
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Performance Tips: | Quantization and appropriate hardware usage can significantly boost performance. |
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Release Notes |
Version: | |
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Notes: | Converted using ct2-transformers-converter with quantization float16. |
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