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@@ -7,7 +7,7 @@ datasets:
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  metrics:
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  - accuracy
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  model-index:
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- - name: codellama_CodeLlama-7b-hf_08_27_23_15_32_28
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  results:
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  - task:
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  name: Causal Language Modeling
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  value: 0.8968056729128353
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  ---
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- <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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- should probably proofread and complete it, then remove this comment. -->
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-
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- # codellama_CodeLlama-7b-hf_08_27_23_15_32_28
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  This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the AshtonIsNotHere/nlp_pp_code_dataset dataset.
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  It achieves the following results on the evaluation set:
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  ## Model description
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- More information needed
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  ## Intended uses & limitations
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  ## Training and evaluation data
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- More information needed
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  ## Training procedure
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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  - Transformers 4.30.2
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  - Pytorch 2.0.1+cu117
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  - Datasets 2.13.0
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- - Tokenizers 0.13.3
 
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  metrics:
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  - accuracy
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  model-index:
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+ - name: CodeLlama_7B_nlp_pp
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  results:
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  - task:
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  name: Causal Language Modeling
 
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  value: 0.8968056729128353
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  ---
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+ # CodeLlama_7B_nlp_pp
 
 
 
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  This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the AshtonIsNotHere/nlp_pp_code_dataset dataset.
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  It achieves the following results on the evaluation set:
 
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  ## Model description
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+ This model has been fine-tuned for code completion on a dataset of NLP++ code.
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  ## Intended uses & limitations
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  ## Training and evaluation data
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+ Dataset consists of a combination of scraped NLP++ code and NLP++ code examples from the [VisualText website](https://visualtext.org/help/).
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  ## Training procedure
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+ This model is trained in a multinode, multi-gpu setup with DeepSpeed Z3. For more information on the training setup, check out the [GitHub repo](https://github.com/ashtonomy/nlp_pp_code_completion).
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+
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
 
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  - Transformers 4.30.2
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  - Pytorch 2.0.1+cu117
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  - Datasets 2.13.0
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+ - Tokenizers 0.13.3