metadata

pipeline_tag: text-generation
inference: true
widget:
  - text: 'def print_hello_world():'
    example_title: Hello world
    group: Python
  - text: Gradient descent is
    example_title: Machine Learning
    group: English
  - license: bigcode-openrail-m
datasets:
  - bigcode/the-stack-dedup
  - tiiuae/falcon-refinedweb
metrics:
  - code_eval
  - mmlu
  - arc
  - hellaswag
  - truthfulqa
library_name: transformers
tags:
  - code
model-index:
  - name: StarCoderPlus
    results:
      - task:
          type: text-generation
        dataset:
          type: openai_humaneval
          name: HumanEval (Prompted)
        metrics:
          - name: pass@1
            type: pass@1
            value: 26.7
            verified: false
      - task:
          type: text-generation
        dataset:
          type: MMLU (5-shot)
          name: MMLU
        metrics:
          - name: Accuracy
            type: Accuracy
            value: 45.1
            verified: false
      - task:
          type: text-generation
        dataset:
          type: HellaSwag (10-shot)
          name: HellaSwag
        metrics:
          - name: Accuracy
            type: Accuracy
            value: 77.3
            verified: false
      - task:
          type: text-generation
        dataset:
          type: ARC (25-shot)
          name: ARC
        metrics:
          - name: Accuracy
            type: Accuracy
            value: 48.9
            verified: false
      - task:
          type: text-generation
        dataset:
          type: ThrutfulQA (0-shot)
          name: ThrutfulQA
        metrics:
          - name: Accuracy
            type: Accuracy
            value: 37.9
            verified: false
extra_gated_prompt: >-
  ## Model License Agreement

  Please read the BigCode [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

StarCoderPlus

Play with the instruction-tuned StarCoderPlus at StarChat-Beta.

Model Summary
Use
Limitations
Training
License

Model Summary

StarCoderPlus is a fine-tuned version of StarCoderBase on 600B tokens from the English web dataset RedefinedWeb combined with StarCoderData from The Stack (v1.2) and a Wikipedia dataset. It's a 15.5B parameter Language Model trained on English and 80+ programming languages. The model uses Multi Query Attention, a context window of 8192 tokens, and was trained using the Fill-in-the-Middle objective on 1.6 trillion tokens.

Repository: bigcode/Megatron-LM
Project Website: bigcode-project.org
Point of Contact: [email protected]
Languages: English & 80+ Programming languages

Use

Intended use

The model was trained on English and 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, the instruction-tuned version in StarChat makes a capable assistant.

Feel free to share your generations in the Community tab!

Generation

# pip install -q transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

checkpoint = "bigcode/starcoderplus"
device = "cuda" # for GPU usage or "cpu" for CPU usage

tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint).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:

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 training code 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 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 a mixture of English text from the web and GitHub code. Therefore it might encounter limitations when working with non-English text, and can carry the stereotypes and biases commonly encountered online. Additionally, the generated code should be used with caution as it may contain errors, inefficiencies, or potential vulnerabilities. For a more comprehensive understanding of the base model's code limitations, please refer to See StarCoder paper.

Training

StarCoderPlus is a fine-tuned version on 600B English and code tokens of StarCoderBase, which was pre-trained on 1T code tokens. Below are the fine-tuning details:

Model

Architecture: GPT-2 model with multi-query attention and Fill-in-the-Middle objective
Finetuning steps: 150k
Finetuning tokens: 600B
Precision: bfloat16

Hardware

GPUs: 512 Tesla A100
Training time: 14 days

Software

Orchestration: Megatron-LM
Neural networks: PyTorch
BP16 if applicable: apex

License

The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement here.

bigcode
/

starcoderplus