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---
license: apache-2.0
datasets:
- bigcode/starcoderdata
---
# 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](https://huggingface.co/datasets/bigcode/starcoderdata).
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](https://www.apache.org/licenses/LICENSE-2.0)
## Documentation
- Blog Post: [Introducing DeciCoder-6B: Code LLM Engineered for Accuracy & Cost Efficiency At Scale](https://deci.ai/blog/decicoder-6b-the-best-multi-language-code-generation-llm-in-its-class/)
- Tutorial: [How to Run DeciCoder-6B on Qualcomm Cloud AI 100](https://github.com/quic/cloud-ai-sdk/tree/1.12/models/language_processing/decoder)
- Google Colab [Notebook](http://bit.ly/DeciCoder-6B-Notebook-1)
- Run DeciCoder on [AWS DL2q instances using the Qualcomm Cloud AI Platform SDK](https://bit.ly/Amazon-EC2-DL2q-Instance)
- Questions: Feel free to contact us via our [Discord Community!](https://discord.com/invite/p9ecgRhDR8/)
## 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](https://arxiv.org/abs/2104.09864)
### How to Use
```bibtex
# 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.
```bibtex
@misc{DeciFoundationModels,
title = {DeciCoder-6B},
author = {DeciAI Research Team},
year = {2024}
url={[https://huggingface.co/deci/decicoder-6B](https://huggingface.co/deci/decicoder-6B)},
}
``` |