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Model Card for DeciCoder-6B

DeciCoder-6B is a 6 billion parameter decoder-only code completion model trained on the Python, Java, Javascript, Rust, C++, C, and C# subset of Starcoder Training Dataset. The model uses variable Grouped Query Attention and has a context window of 2k tokens. It was trained using a Fill-in-the-Middle training objective. The model's architecture was generated by Deci's proprietary Neural Architecture Search-based technology, AutoNAC.

Model Details

  • Developed by: Deci
  • Model type: DeciCoder-6B is an auto-regressive language model based on the transformer decoder architecture, using variable Grouped Query Attention.
  • Language(s): Python, Java, JavaScript, Rust, C++, C, C#, Go
  • License: Model checkpoints are licensed under the Apache 2.0

Documentation

Model Architecture

Parameters Layers Heads Sequence Length GQA num_key_value_heads
6B 32 32 2k Variable
  • Decoder layer: Variable Grouped Query Attention
  • Position Embeddings: Rotary Position Embeddings Su et al., 2021

How to Use

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

checkpoint = "Deci/DeciCoder-6B"
device = "cuda" # for GPU usage or "cpu" for CPU usage

tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint, torch_dtype=torch.bfloat16, trust_remote_code=True).to(device)

inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to(device)
outputs = model.generate(inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0]))

### Attribution

DeciCoder-6B was trained on StarCoder Training Dataset, filtered for
Python, Java, JavaScript, Ruby, RUST, C++, C, and C#. For additional information, please
refer to [https://huggingface.co/datasets/bigcode/starcoderdata](https://huggingface.co/datasets/bigcode/starcoderdata).

Limitations

The model has undergone training with source code from Python, Java, JavaScript, RUST, C++, C, and C#, and Go. While the primary language in the source is English, it does contain other languages. Therefore, the model can produce code snippets given some context. However, there is no assurance that the resulting code will function as expected. It might be suboptimal, contain bugs, or even exploits.

Evaluation

Below are DeciCoder-6B's pass@1 on MultiPL HumanEval scores

Python JavaScript Java C++ C# Rust Go
33.3% 29.3% 30.3% 29.93% 20.31% 20.5% 77.47%

Runtime Benchmarks

Inference Tool Hardware Prompt Length Generation Length Throughput (tokens/sec)
Qualcomm Cloud AI 100 SDK Qualcomm Cloud AI 100 1024 1024 531.3
  • Measured for maximal batch size on the device

How to Cite

Please cite this model using this format.

@misc{DeciFoundationModels,
title = {DeciCoder-6B},
author = {DeciAI Research Team},
year = {2024}
url={[https://huggingface.co/deci/decicoder-6B](https://huggingface.co/deci/decicoder-6B)},
}
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