gpt_bigcode-santacoder

About

What gpt_bigcode-santacoder is

SantaCoder Play with the model on the SantaCoder Space Demo. Table of Contents 1. Model Summary 2. Use 3. Limitations 4. Training 5. License 6. Citation Model Summary This is the same model as SantaCoder but it can be loaded with transformers =4.28.1 to use the GPTBigCode architecture. We refer the reader to the SantaCoder model page for full documentation about this model - Repository: bigcode/Megatron-LM - Project Website: bigcode-project.org - Paper: 🎅SantaCoder: Don't reach for the stars!🌟 - Point of Contact: contact@bigcode-project.org - Languages: Python, Java, and JavaScript There are two versions (branches) of the model: main: Uses the gptbigcode model. Requires the bigcode fork of transformers. maincustom: Packaged with its modeling code. Requires transformers=4.27. Alternatively, it can run on older versions by setting the configuration parameter activationfunction = "gelupytorchtanh". 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. 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. 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 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 an

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

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Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Benchmarks

Reported results

As published on the model card — the maker's own numbers, not measured by AxForge.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys gpt-bigcode-santacoder for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (gpt-bigcode-santacoder below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"gpt-bigcode-santacoder","messages":[{"role":"user","content":"Hello"}]}'

Details

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Licence

Open weights

Sources

Weights & code

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