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metadata
license: other
language:
  - code
programming_language:
  - Java
  - JavaScript
  - Python
pipeline_tag: text-generation
widget:
  - text: 'def print_hello_world():'
    example_title: Hello world
    group: Python
model-index:
  - name: SantaCoder
    results:
      - task:
          type: text-generation
        dataset:
          type: openai_humaneval
          name: HumanEval (Python)
        metrics:
          - name: pass@1
            type: pass@1
            value: 0
            verified: false
          - name: pass@10
            type: pass@10
            value: 0
            verified: false
          - name: pass@100
            type: pass@100
            value: 0
            verified: false
      - task:
          type: text-generation
        dataset:
          type: nuprl/MultiPL-E
          name: MultiPL HumanEval (Java)
        metrics:
          - name: pass@1
            type: pass@1
            value: 0
            verified: false
          - name: pass@10
            type: pass@10
            value: 0
            verified: false
          - name: pass@100
            type: pass@100
            value: 0
      - task:
          type: text-generation
        dataset:
          type: nuprl/MultiPL-E
          name: MultiPL HumanEval (JavaScript)
        metrics:
          - name: pass@1
            type: pass@1
            value: 0
            verified: false
          - name: pass@10
            type: pass@10
            value: 0
            verified: false
          - name: pass@100
            type: pass@100
            value: 0
      - task:
          type: text-generation
        dataset:
          type: openai_humaneval
          name: MBPP (Python)
        metrics:
          - name: pass@1
            type: pass@1
            value: 0
            verified: false
          - name: pass@10
            type: pass@10
            value: 0
            verified: false
          - name: pass@100
            type: pass@100
            value: 0
            verified: false
      - task:
          type: text-generation
        dataset:
          type: nuprl/MultiPL-E
          name: MultiPL MBPP (Java)
        metrics:
          - name: pass@1
            type: pass@1
            value: 0
            verified: false
          - name: pass@10
            type: pass@10
            value: 0
            verified: false
          - name: pass@100
            type: pass@100
            value: 0
      - task:
          type: text-generation
        dataset:
          type: nuprl/MultiPL-E
          name: MultiPL MBPP (JavaScript)
        metrics:
          - name: pass@1
            type: pass@1
            value: 0
            verified: false
          - name: pass@10
            type: pass@10
            value: 0
            verified: false
          - name: pass@100
            type: pass@100
            value: 0
      - task:
          type: text-generation
        dataset:
          type: loubnabnl/humaneval_infilling
          name: HumanEval FIM (Python)
        metrics:
          - name: pass@1
            type: pass@1
            value: 0
            verified: false
          - name: pass@10
            type: pass@10
            value: 0
            verified: false
          - name: pass@100
            type: pass@100
            value: 0
            verified: false
      - task:
          type: text-generation
        dataset:
          type: nuprl/MultiPL-E
          name: MultiPL HumanEval FIM (Java)
        metrics:
          - name: pass@1
            type: pass@1
            value: 0
            verified: false
          - name: pass@10
            type: pass@10
            value: 0
            verified: false
          - name: pass@100
            type: pass@100
            value: 0
      - task:
          type: text-generation
        dataset:
          type: nuprl/MultiPL-E
          name: MultiPL HumanEval FIM (JavaScript)
        metrics:
          - name: pass@1
            type: pass@1
            value: 0
            verified: false
          - name: pass@10
            type: pass@10
            value: 0
            verified: false
          - name: pass@100
            type: pass@100
            value: 0
      - task:
          type: text-generation
        dataset:
          type: code_x_glue_ct_code_to_text
          name: CodeXGLUE code-to-text (Python)
        metrics:
          - name: BLEU
            type: bleu
            value: 0
            verified: false

SantaCoder

banner

Table of Contents

  1. Model Summary
  2. Use
  3. Limitations
  4. Training
  5. Citation

Model Summary

The SantaCoder models are a series of 1B parameter models trained on Python, Java, and JavaScript. They were trained on datasets with different filter parameters and with architecture and objective variations. The main model uses multi-query attention, was trained using near-deduplication and commnent-to-code ratio as filtering criteria and using the Fill-in-the-Middle objective.

|Model|Architecture|Objective|Filtering| |:-|:-|:-|:-|:-| |mha|MHA|AR + FIM| Base | |no-fim| MQA | AR| Base | |fim| MQA | AR + FIM | Base | |stars| MQA | AR + FIM | GitHub stars | |fertility| MQA | AR + FIM | Tokenizer fertility | |comments| MQA | AR + FIM | Comment-to-code ratio | |dedup-alt| MQA | AR + FIM | Stronger near-deduplication | |dedup-alt-comments| MQA | AR + FIM | Stronger near-deduplication and comment-to-code ratio |

The dedup-alt-comments model is the best performing model and was trained twice as long as the others. This checkpoint is available here on the main

Use

Intended use

Feel free to share your generations in the Community tab!

How to use

Generation 

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

checkpoint = "bigcode/santacoder"
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()

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-mid uses special tokens to identify the prefix/middle/suffic 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]))

Load other checkpoints

We upload the checkpoint of each experiment to a seperate branch as well as the intermediate checkpoints as commits on the branches. You can load them with the revision flag:

checkpoint = "bigcode/santacoder"
revision = "no-fim" # name of branch or commit hash

model = AutoModelForCausalLM.from_pretrained(checkpoint, revision=revision, trust_remote_code=True).to(device)

Attribution

The pretraining dataset of the model was filtered for permissive licenses only. Nevertheless, the model can generate source code verbatim from the dataset which requires attribution. 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 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.

Training

Model

  • Architecture: GPT-2 model with multi-query attention and Fill-in-the-Middle objective
  • Pretraining steps: 600K
  • Pretraining tokens: 236 billion
  • Precision: float16

Hardware

  • GPUs: 96 Tesla V100
  • Training time: 6.2 days
  • Total FLOPS: 2.1 x 10e21

Software

Citation

TODO