Text2Text Generation
NeMo
PyTorch
English
causal-lm
okuchaiev commited on
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  ## Model Description
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- Megatron-GPT 1.3B is a transformer-based language model. GPT refers to a class of transformer decoder-only models similar to GPT-2 and 3 while 1.3B refers to the total trainable parameter count (1.3 Billion) [1, 2].
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  This model was trained with [NeMo Megatron](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/nemo_megatron/intro.html).
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  ## Training Data
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- The model was trained on ["The Piles" dataset prepared by Eleuther.AI](https://pile.eleuther.ai/).
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  ## Evaluation results
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- *Zero-shot performance.*
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  | ARC-Challenge | ARC-Easy | RACE-middle | RACE-high | Winogrande | RTE | BoolQA | HellaSwag | PiQA |
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  | ------------- | -------- | ----------- | --------- | ---------- | --- | ------ | --------- | ---- |
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- | 0.3012 | 0.4596 | 0.459 | 0.3811 | 0.5343 | 0.5451 | 0.5979 | 0.4442 | 0.6834 |
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  ## References
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  [3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)
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  ## Licence
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  License to use this model is covered by the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). By downloading the public and release version of the model, you accept the terms and conditions of the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) license.
 
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  ## Model Description
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+ Megatron-GPT 1.3B is a transformer-based language model. GPT refers to a class of transformer decoder-only models similar to GPT-2 and 3 while 1.3B refers to the total trainable parameter count (1.3 Billion) [1, 2]. It has Tensor Parallelism (TP) of 1, Pipeline Parallelism (PP) of 1 and should fit on a single NVIDIA GPU.
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  This model was trained with [NeMo Megatron](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/nemo_megatron/intro.html).
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  ## Training Data
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+ The model was trained on ["The Piles" dataset prepared by Eleuther.AI](https://pile.eleuther.ai/). [4]
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  ## Evaluation results
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+ *Zero-shot performance.* Evaluated using [LM Evaluation Test Suite from AI21](https://github.com/AI21Labs/lm-evaluation)
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  | ARC-Challenge | ARC-Easy | RACE-middle | RACE-high | Winogrande | RTE | BoolQA | HellaSwag | PiQA |
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  | ------------- | -------- | ----------- | --------- | ---------- | --- | ------ | --------- | ---- |
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+ | 0.3012 | 0.4596 | 0.459 | 0.3797 | 0.5343 | 0.5451 | 0.5979 | 0.4443 | 0.6834 |
 
 
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  ## References
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  [3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)
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+ [4] [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://arxiv.org/abs/2101.00027)
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  ## Licence
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  License to use this model is covered by the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). By downloading the public and release version of the model, you accept the terms and conditions of the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) license.