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Model: bigcode/gpt_bigcode-santacoder Source: Original Platform
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README.md
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README.md
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---
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license: openrail
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datasets:
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- bigcode/the-stack
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language:
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- code
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programming_language:
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- Java
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- JavaScript
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- Python
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pipeline_tag: text-generation
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inference: false
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model-index:
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- name: SantaCoder
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results:
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- task:
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type: text-generation
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dataset:
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type: nuprl/MultiPL-E
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name: MultiPL HumanEval (Python)
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metrics:
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- name: pass@1
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type: pass@1
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value: 0.18
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verified: false
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- name: pass@10
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type: pass@10
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value: 0.29
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verified: false
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- name: pass@100
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type: pass@100
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value: 0.49
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verified: false
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- task:
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type: text-generation
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dataset:
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type: nuprl/MultiPL-E
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name: MultiPL MBPP (Python)
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metrics:
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- name: pass@1
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type: pass@1
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value: 0.35
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verified: false
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- name: pass@10
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type: pass@10
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value: 0.58
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verified: false
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- name: pass@100
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type: pass@100
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value: 0.77
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verified: false
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- task:
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type: text-generation
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dataset:
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type: nuprl/MultiPL-E
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name: MultiPL HumanEval (JavaScript)
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metrics:
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- name: pass@1
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type: pass@1
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value: 0.16
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verified: false
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- name: pass@10
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type: pass@10
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value: 0.27
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verified: false
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- name: pass@100
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type: pass@100
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value: 0.47
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verified: false
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- task:
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type: text-generation
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dataset:
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type: nuprl/MultiPL-E
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name: MultiPL MBPP (Javascript)
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metrics:
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- name: pass@1
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type: pass@1
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value: 0.28
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verified: false
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- name: pass@10
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type: pass@10
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value: 0.51
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verified: false
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- name: pass@100
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type: pass@100
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value: 0.70
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verified: false
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- task:
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type: text-generation
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dataset:
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type: nuprl/MultiPL-E
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name: MultiPL HumanEval (Java)
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metrics:
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- name: pass@1
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type: pass@1
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value: 0.15
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verified: false
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- name: pass@10
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type: pass@10
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value: 0.26
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verified: false
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- name: pass@100
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type: pass@100
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value: 0.41
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verified: false
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- task:
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type: text-generation
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dataset:
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type: nuprl/MultiPL-E
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name: MultiPL MBPP (Java)
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metrics:
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- name: pass@1
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type: pass@1
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value: 0.28
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verified: false
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- name: pass@10
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type: pass@10
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value: 0.44
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verified: false
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- name: pass@100
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type: pass@100
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value: 0.59
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verified: false
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- task:
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type: text-generation
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dataset:
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type: loubnabnl/humaneval_infilling
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name: HumanEval FIM (Python)
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metrics:
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- name: single_line
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type: exact_match
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value: 0.44
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verified: false
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- task:
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type: text-generation
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dataset:
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type: nuprl/MultiPL-E
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name: MultiPL HumanEval FIM (Java)
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metrics:
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- name: single_line
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type: exact_match
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value: 0.62
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verified: false
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- task:
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type: text-generation
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dataset:
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type: nuprl/MultiPL-E
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name: MultiPL HumanEval FIM (JavaScript)
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metrics:
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- name: single_line
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type: exact_match
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value: 0.60
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verified: false
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- task:
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type: text-generation
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dataset:
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type: code_x_glue_ct_code_to_text
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name: CodeXGLUE code-to-text (Python)
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metrics:
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- name: BLEU
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type: bleu
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value: 18.13
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verified: false
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---
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# SantaCoder
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Play with the model on the [SantaCoder Space Demo](https://huggingface.co/spaces/bigcode/santacoder-demo).
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# Table of Contents
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1. [Model Summary](#model-summary)
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2. [Use](#use)
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3. [Limitations](#limitations)
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4. [Training](#training)
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5. [License](#license)
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6. [Citation](#citation)
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# Model Summary
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This is the same model as [SantaCoder](https://huggingface.co/bigcode/santacoder) but it can be loaded with transformers >=4.28.1 to use the GPTBigCode architecture.
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We refer the reader to the [SantaCoder model page](https://huggingface.co/bigcode/santacoder) for full documentation about this model
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- **Repository:** [bigcode/Megatron-LM](https://github.com/bigcode-project/Megatron-LM)
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- **Project Website:** [bigcode-project.org](www.bigcode-project.org)
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- **Paper:** [🎅SantaCoder: Don't reach for the stars!🌟](https://t.co/YV3pzUbYOr)
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- **Point of Contact:** [contact@bigcode-project.org](mailto:contact@bigcode-project.org)
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- **Languages:** Python, Java, and JavaScript
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There are two versions (branches) of the model:
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* `main`: Uses the `gpt_bigcode` model. [Requires the bigcode fork of transformers](https://github.com/bigcode-project/transformers).
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* `main_custom`: Packaged with its modeling code. Requires `transformers>=4.27`.
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Alternatively, it can run on older versions by setting the configuration parameter `activation_function = "gelu_pytorch_tanh"`.
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# Use
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## Intended use
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The model was trained on GitHub code. As such it is _not_ an instruction model and commands like "Write a function that computes the square root." do not work well.
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You should phrase commands like they occur in source code such as comments (e.g. `# the following function computes the sqrt`) or write a function signature and docstring and let the model complete the function body.
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### Attribution & Other Requirements
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The pretraining dataset of the model was filtered for permissive licenses only. Nevertheless, the model can generate source code verbatim from the dataset. The code's license might require attribution and/or other specific requirements that must be respected. We provide a [search index](https://huggingface.co/spaces/bigcode/santacoder-search) that let's you search through the pretraining data to identify where generated code came from and apply the proper attribution to your code.
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# Limitations
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The model has been trained on source code in Python, Java, and JavaScript. The predominant language in source is English although other languages are also present. As such the model is capable to generate code snippets provided some context but the generated code is not guaranteed to work as intended. It can be inefficient, contain bugs or exploits.
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# Training
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## Model
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- **Architecture:** GPT-2 model with multi-query attention and Fill-in-the-Middle objective
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- **Pretraining steps:** 600K
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- **Pretraining tokens:** 236 billion
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- **Precision:** float16
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## Hardware
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- **GPUs:** 96 Tesla V100
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- **Training time:** 6.2 days
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- **Total FLOPS:** 2.1 x 10e21
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## Software
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- **Orchestration:** [Megatron-LM](https://github.com/bigcode-project/Megatron-LM)
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- **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch)
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- **FP16 if applicable:** [apex](https://github.com/NVIDIA/apex)
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# License
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The model is licenses under the CodeML Open RAIL-M v0.1 license. You can find the full license [here](https://huggingface.co/spaces/bigcode/license).
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32
config.json
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config.json
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{
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"activation_function": "gelu_pytorch_tanh",
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"architectures": [
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"GPTBigCodeForCausalLM"
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],
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"attention_softmax_in_fp32": true,
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"multi_query": true,
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"attn_pdrop": 0.1,
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"bos_token_id": 49152,
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"embd_pdrop": 0.1,
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"eos_token_id": 49152,
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"initializer_range": 0.02,
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"layer_norm_epsilon": 1e-05,
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"model_type": "gpt_bigcode",
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"n_embd": 2048,
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"n_head": 16,
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"n_inner": 8192,
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"n_layer": 24,
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"n_positions": 2048,
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"resid_pdrop": 0.1,
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"runner_max_sequence_length": null,
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"scale_attention_softmax_in_fp32": true,
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"scale_attn_weights": true,
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"summary_activation": null,
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"summary_first_dropout": 0.1,
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"summary_proj_to_labels": true,
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"summary_type": "cls_index",
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"summary_use_proj": true,
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"transformers_version": "4.28.0.dev0",
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"use_cache": true,
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"vocab_size": 49280
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}
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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pytorch_model.bin
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special_tokens_map.json
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{
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"additional_special_tokens": [
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"<|endoftext|>",
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"<fim-prefix>",
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"<fim-middle>",
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"<fim-suffix>",
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"<fim-pad>"
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]
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}
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tokenizer.json
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tokenizer.json
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size 2081116
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{
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"errors": "replace",
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"tokenizer_class": "GPT2TokenizerFast",
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"bos_token": "<|endoftext|>",
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"eos_token": "<|endoftext|>",
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"model_max_length": 2048
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}
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