| --- |
| pipeline_tag: text-generation |
| inference: true |
| widget: |
| - text: 'def print_hello_world():' |
| example_title: Hello world |
| group: Python |
| license: openrail |
| datasets: |
| - bigcode/the-stack-dedup |
| metrics: |
| - code_eval |
| library_name: transformers |
| tags: |
| - code |
| model-index: |
| - name: StarCoder |
| results: |
| - task: |
| type: text-generation |
| dataset: |
| type: openai_humaneval |
| name: HumanEval |
| metrics: |
| - name: pass@1 |
| type: pass@1 |
| value: 0.336 |
| verified: false |
| - task: |
| type: text-generation |
| dataset: |
| type: mbpp |
| name: MBPP |
| metrics: |
| - name: pass@1 |
| type: pass@1 |
| value: 0.527 |
| verified: false |
| - task: |
| type: text-generation |
| dataset: |
| type: ds1000 |
| name: DS-1000 (Overall Completion) |
| metrics: |
| - name: pass@1 |
| type: pass@1 |
| value: 0.2 |
| verified: false |
| - task: |
| type: text-generation |
| dataset: |
| type: nuprl/MultiPL-E |
| name: MultiPL-HumanEval (C++) |
| metrics: |
| - name: pass@1 |
| type: pass@1 |
| value: 0.3155 |
| verified: false |
| - task: |
| type: text-generation |
| dataset: |
| type: nuprl/MultiPL-E |
| name: MultiPL-HumanEval (C#) |
| metrics: |
| - name: pass@1 |
| type: pass@1 |
| value: 0.2101 |
| verified: false |
| - task: |
| type: text-generation |
| dataset: |
| type: nuprl/MultiPL-E |
| name: MultiPL-HumanEval (D) |
| metrics: |
| - name: pass@1 |
| type: pass@1 |
| value: 0.1357 |
| verified: false |
| - task: |
| type: text-generation |
| dataset: |
| type: nuprl/MultiPL-E |
| name: MultiPL-HumanEval (Go) |
| metrics: |
| - name: pass@1 |
| type: pass@1 |
| value: 0.1761 |
| verified: false |
| - task: |
| type: text-generation |
| dataset: |
| type: nuprl/MultiPL-E |
| name: MultiPL-HumanEval (Java) |
| metrics: |
| - name: pass@1 |
| type: pass@1 |
| value: 0.3022 |
| verified: false |
| - task: |
| type: text-generation |
| dataset: |
| type: nuprl/MultiPL-E |
| name: MultiPL-HumanEval (Julia) |
| metrics: |
| - name: pass@1 |
| type: pass@1 |
| value: 0.2302 |
| verified: false |
| - task: |
| type: text-generation |
| dataset: |
| type: nuprl/MultiPL-E |
| name: MultiPL-HumanEval (JavaScript) |
| metrics: |
| - name: pass@1 |
| type: pass@1 |
| value: 0.3079 |
| verified: false |
| - task: |
| type: text-generation |
| dataset: |
| type: nuprl/MultiPL-E |
| name: MultiPL-HumanEval (Lua) |
| metrics: |
| - name: pass@1 |
| type: pass@1 |
| value: 0.2389 |
| verified: false |
| - task: |
| type: text-generation |
| dataset: |
| type: nuprl/MultiPL-E |
| name: MultiPL-HumanEval (PHP) |
| metrics: |
| - name: pass@1 |
| type: pass@1 |
| value: 0.2608 |
| verified: false |
| - task: |
| type: text-generation |
| dataset: |
| type: nuprl/MultiPL-E |
| name: MultiPL-HumanEval (Perl) |
| metrics: |
| - name: pass@1 |
| type: pass@1 |
| value: 0.1734 |
| verified: false |
| - task: |
| type: text-generation |
| dataset: |
| type: nuprl/MultiPL-E |
| name: MultiPL-HumanEval (Python) |
| metrics: |
| - name: pass@1 |
| type: pass@1 |
| value: 0.3357 |
| verified: false |
| - task: |
| type: text-generation |
| dataset: |
| type: nuprl/MultiPL-E |
| name: MultiPL-HumanEval (R) |
| metrics: |
| - name: pass@1 |
| type: pass@1 |
| value: 0.1550 |
| verified: false |
| - task: |
| type: text-generation |
| dataset: |
| type: nuprl/MultiPL-E |
| name: MultiPL-HumanEval (Ruby) |
| metrics: |
| - name: pass@1 |
| type: pass@1 |
| value: 0.0124 |
| verified: false |
| - task: |
| type: text-generation |
| dataset: |
| type: nuprl/MultiPL-E |
| name: MultiPL-HumanEval (Racket) |
| metrics: |
| - name: pass@1 |
| type: pass@1 |
| value: 0.0007 |
| verified: false |
| - task: |
| type: text-generation |
| dataset: |
| type: nuprl/MultiPL-E |
| name: MultiPL-HumanEval (Rust) |
| metrics: |
| - name: pass@1 |
| type: pass@1 |
| value: 0.2184 |
| verified: false |
| - task: |
| type: text-generation |
| dataset: |
| type: nuprl/MultiPL-E |
| name: MultiPL-HumanEval (Scala) |
| metrics: |
| - name: pass@1 |
| type: pass@1 |
| value: 0.2761 |
| verified: false |
| - task: |
| type: text-generation |
| dataset: |
| type: nuprl/MultiPL-E |
| name: MultiPL-HumanEval (Bash) |
| metrics: |
| - name: pass@1 |
| type: pass@1 |
| value: 0.1046 |
| verified: false |
| - task: |
| type: text-generation |
| dataset: |
| type: nuprl/MultiPL-E |
| name: MultiPL-HumanEval (Swift) |
| metrics: |
| - name: pass@1 |
| type: pass@1 |
| value: 0.2274 |
| verified: false |
| - task: |
| type: text-generation |
| dataset: |
| type: nuprl/MultiPL-E |
| name: MultiPL-HumanEval (TypeScript) |
| metrics: |
| - name: pass@1 |
| type: pass@1 |
| value: 0.3229 |
| verified: false |
|
|
| extra_gated_prompt: >- |
| ## Model License Agreement |
| |
| Please read the BigScience [OpenRAIL-M license](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) agreement before accepting it. |
| |
| extra_gated_fields: |
| I accept the above license agreement, and will use the Model complying with the set of use restrictions and sharing requirements: checkbox |
|
|
| --- |
| |
|
|
| # StarCoder |
|
|
|  |
|
|
| Play with the model on the [StarCoder Playground](https://huggingface.co/spaces/bigcode/bigcode-playground). |
|
|
| ## Table of Contents |
|
|
| 1. [Model Summary](##model-summary) |
| 2. [Use](##use) |
| 3. [Limitations](##limitations) |
| 4. [Training](##training) |
| 5. [License](##license) |
| 6. [Citation](##citation) |
|
|
| ## Model Summary |
|
|
| The StarCoder models are a series of 15.5B parameter models trained on 80+ programming langues from [The Stack (v1.2)](https://huggingface.co/datasets/bigcode/the-stack) (excluding opt-out requests). |
| The model uses [Multi Query Attention](https://arxiv.org/abs/1911.02150), was trained using the [Fill-in-the-Middle objective](https://arxiv.org/abs/2207.14255) and with 8,192 tokens context window for 1 trillion tokens on heavily deduplicated data. |
|
|
| - **Repository:** [bigcode/Megatron-LM](https://github.com/bigcode-project/Megatron-LM) |
| - **Project Website:** [bigcode-project.org](https://www.bigcode-project.org) |
| - **Paper:** [💫StarCoder: May the source be with you!]() |
| - **Point of Contact:** [contact@bigcode-project.org](mailto:contact@bigcode-project.org) |
| - **Languages:** 80+ Programming languages |
|
|
|
|
| ## Use |
|
|
| ### Intended use |
|
|
| 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. However, by using the [Tech Assistant prompt](https://huggingface.co/datasets/bigcode/ta-prompt) you can turn it into a capable technical assistant. |
|
|
| **Feel free to share your generations in the Community tab!** |
|
|
| ### Generation |
| ```python |
| # pip install -q transformers |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| |
| checkpoint = "bigcode/StarCoder" |
| device = "cuda" # for GPU usage or "cpu" for CPU usage |
| |
| tokenizer = AutoTokenizer.from_pretrained(checkpoint) |
| model = AutoModelForCausalLM.from_pretrained(checkpoint, trust_remote_code=True).to(device) |
| |
| inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to(device) |
| outputs = model.generate(inputs) |
| print(tokenizer.decode(outputs[0])) |
| ``` |
|
|
| ### Fill-in-the-middle |
| Fill-in-the-middle uses special tokens to identify the prefix/middle/suffix part of the input and output: |
|
|
| ```python |
| input_text = "<fim-prefix>def print_hello_world():\n <fim-suffix>\n print('Hello world!')<fim-middle>" |
| inputs = tokenizer.encode(input_text, return_tensors="pt").to(device) |
| outputs = model.generate(inputs) |
| print(tokenizer.decode(outputs[0])) |
| ``` |
|
|
| ### Attribution & Other Requirements |
|
|
| 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/starcoder-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. |
|
|
| # Limitations |
|
|
| The model has been trained on source code from 80+ programming languages. 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. |
|
|
| # Training |
|
|
| ## Model |
|
|
| - **Architecture:** GPT-2 model with multi-query attention and Fill-in-the-Middle objective |
| - **Pretraining steps:** 250k |
| - **Pretraining tokens:** 1 trillion |
| - **Precision:** bfloat16 |
|
|
| ## Hardware |
|
|
| - **GPUs:** 512 Tesla A100 |
| - **Training time:** 24 days |
|
|
| ## Software |
|
|
| - **Orchestration:** [Megatron-LM](https://github.com/bigcode-project/Megatron-LM) |
| - **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch) |
| - **BP16 if applicable:** [apex](https://github.com/NVIDIA/apex) |
|
|
| # License |
| The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement [here](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement). |
| # Citation |
| ``` |
| # Coming soon. |
| ``` |
|
|
