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README.md
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- pytorch
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- causal-lm
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- code-generation
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license: apache-2.0
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| Hyperparameter | Value |
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| \\(n_{parameters}\\) | 1,331,810,304 |
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| \\(n_{layers}\\) | 24 |
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| \\(d_{model}\\) | 2,048 |
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| \\(d_{ff}\\) | 8,192 |
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| \\(n_{heads}\\) | 16 |
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| \\(d_{head}\\) | 128 |
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| \\(n_{ctx}\\) | 2,048 |
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The model consists of 24 transformer layers with a model dimension of 2048, and a feedforward dimension of 8192. The model
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dimension is split into 16 heads, each with a dimension of 128. Rotary Position Embedding (RoPE) is used.
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The model is trained with the same tokenizer as GPT-NeoX-20b (link here), for a vocabulary of 50254 tokens.
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## Training Data
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The model was trained on the Pile, an 800Gb dataset composed of varied web corpora. The datasheet and paper for the Pile can be found [here] and [here] respectively
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## Training Details
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Middle segments “to infill” were selected uniformly at random from contexts at the character level, and these contexts were then reformatted as
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<SUF> {last 1/3rd of the context} <PRE> {first 1/3rd of the context} <MID> {middle 1/3rd of the context} <EOD>
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Suppose we have some text that we would like to perform infilling on at a certain “cursor location”.
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This would have the form {some prelude text here}
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The way to perform infilling generation would be via placing the input text into this format:
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## Intended Uses and Limitations
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- pytorch
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- causal-lm
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- code-generation
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- The Pile
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license: apache-2.0
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| Hyperparameter | Value |
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|----------------------|----------------------------------------------------------------------------------------------------------------------------------------|
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| \\(n_{parameters}\\) | 1,331,810,304 |
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| \\(n_{layers}\\) | 24 |
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| \\(d_{model}\\) | 2048 |
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| \\(d_{ff}\\) | 8192 |
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| \\(n_{heads}\\) | 16 |
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| \\(d_{head}\\) | 128 |
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| \\(n_{ctx}\\) | 2048 |
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| \\(n_{vocab}\\) | 50254 |
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| Positional Encoding | [Rotary Position Embedding (RoPE)](https://arxiv.org/abs/2104.09864)
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The model consists of 24 transformer layers with a hidden dimension of 2048, and a feedforward intermediate dimension of 8192. The hidden dimension is split into 16 heads for self-attention, each with a dimension of 128. Rotary Position Embedding (RoPE) is used.
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The model is trained with the same tokenizer as [GPT-NeoX-20b](https://arxiv.org/abs/2204.06745), for a vocabulary size of 50254 tokens.
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## Training Data
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The model was trained on the Pile, an 800Gb dataset composed of varied web corpora. The datasheet and paper for the Pile can be found [here](https://arxiv.org/abs/2201.07311) and [here](https://arxiv.org/abs/2101.00027) respectively.
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## Training Details
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Middle segments “to infill” were selected uniformly at random from contexts at the character level, and these contexts were then reformatted as
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\<SUF\> {last 1/3rd of the context} \<PRE\> {first 1/3rd of the context} \<MID\> {middle 1/3rd of the context} \<EOD\>
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Suppose we have some text that we would like to perform infilling on at a certain “cursor location”.
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This would have the form {some prelude text here} \<INFILLING LOCATION\> {some text following cursor}.
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The way to perform infilling generation would be via placing the input text into this format:
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\<SUF\> {some text following cursor} \<PRE\> {some prelude text here} \<MID\> ... language model output is generated after \<MID\> token!
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## Intended Uses and Limitations
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