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README.md
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# Model Summary
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The SantaCoder models are a series of 1B parameter models trained on the Python, Java, and JavaScript subset of [The Stack (v1.1)](https://huggingface.co/datasets/bigcode/the-stack) (which excluded opt-out requests).
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The main model uses multi-query attention, was trained using near-deduplication and comment-to-code ratio as filtering criteria and using the Fill-in-the-Middle objective.
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In addition there are several models that were trained on datasets with different filter parameters and with architecture and objective variations.
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|`fertility`| MQA | AR + FIM | Tokenizer fertility |
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|`comments`| MQA | AR + FIM | Comment-to-code ratio |
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|`dedup-alt`| MQA | AR + FIM | Stronger near-deduplication |
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The `
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# Use
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```
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### Fill-in-the-middle
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Fill-in-the-
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```python
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input_text = "<fim-prefix>def print_hello_world():\n <fim-suffix>\n print('Hello world!')<fim-middle>"
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# Model Summary
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The SantaCoder models are a series of 1.1B parameter models trained on the Python, Java, and JavaScript subset of [The Stack (v1.1)](https://huggingface.co/datasets/bigcode/the-stack) (which excluded opt-out requests).
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The main model uses multi-query attention, was trained using near-deduplication and comment-to-code ratio as filtering criteria and using the Fill-in-the-Middle objective.
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In addition there are several models that were trained on datasets with different filter parameters and with architecture and objective variations.
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|`fertility`| MQA | AR + FIM | Tokenizer fertility |
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|`comments`| MQA | AR + FIM | Comment-to-code ratio |
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|`dedup-alt`| MQA | AR + FIM | Stronger near-deduplication |
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|`final`| MQA | AR + FIM | Stronger near-deduplication and comment-to-code ratio |
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The `final` model is the best performing model and was trained twice as long as the others. This checkpoint is the default model and available on the `main` branch. All other checkpoints are on separate branches with according names.
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# Use
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```
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### Fill-in-the-middle
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Fill-in-the-middle uses special tokens to identify the prefix/middle/suffic part of the input and output:
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```python
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input_text = "<fim-prefix>def print_hello_world():\n <fim-suffix>\n print('Hello world!')<fim-middle>"
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