| Data
| CommitPackFT+OASST |
Filtered version of CommitPack and OASST for high-quality commit messages that resemble instructions |
| Model
| Astraios-1B |
Collection of StarCoderBase-1B models instruction tuned on CommitPackFT + OASST with different tuning methods |
|
| Astraios-3B |
Collection of StarCoderBase-3B (3B parameters) models instruction tuned on CommitPackFT + OASST with different tuning methods |
|
| Astraios-7B |
Collection of StarCoderBase-7B (7B parameters) models instruction tuned on CommitPackFT + OASST with different tuning methods |
|
| Astraios-16B |
Collection of StarCoderBase-16B (16B parameters) models instruction tuned on CommitPackFT + OASST with different tuning methods |
| Evaluation
| BigCloneBench |
Dataset for clone detection; We use 2,000 samples for evaluation |
|
| Devign |
Dataset for defect detection; We use 2,000 samples for evaluation |
|
| HumanEvalPack |
Extension of OpenAI's HumanEval to cover 3 scenarios across 6 languages |
|
| ReCode |
Dataset for the robustness of code generation, covering 4 variants |
|
| Asleep At The Keyboard |
Datasets for security of code generation; We use DoW for evaluation |
# Use
## Intended use
The model follows instructions provided in the input. You should always preface your input with "Question: " and finish it with "Answer:", for example: "Question: Please write a function in Python that performs bubble sort.
Answer:"
**Feel free to share your generations in the Community tab!**
## Generation
```python
# pip install -q transformers
# pip install -e git+https://github.com/bigcode-project/astraios#subdirectory=peft
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
peft_checkpoint = "bigcode/astraios-1b-ptuning"
checkpoint = "bigcode/starcoderbase-1b"
model = AutoModelForCausalLM.from_pretrained(checkpoint)
model = PeftModel.from_pretrained(model, peft_checkpoint)
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("Question: Please write a function in Python that performs bubble sort.
Answer:", return_tensors="pt").to(device)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
# Training
## Model
- **Architecture:** GPT-2 model with multi-query attention and Fill-in-the-Middle objective
- **Steps:** 250k pretraining & 200 instruction tuning
- **Precision:** fp32
## Hardware
- **Pretraining:**
- **GPUs:** 512 Tesla A100
- **Training time:** 24 days
- **Instruction tuning:**
- **GPUs:** 8 Tesla A100
## Software
- **Orchestration:** [Megatron-LM/Transformers](https://github.com/bigcode-project/octopack#training)
- **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch)
# Citation
```bibtex
```
