--- license: bigcode-openrail-m datasets: - bigcode/guanaco-commits metrics: - code_eval library_name: peft tags: - code --- # Astraios: A Recipe for Parameter-Efficient Instruction Tuning Code Language Models

Astraios

# Table of Contents 1. [Model Summary](#model-summary) 2. [Use](#use) 3. [Training](#training) 4. [Citation](#citation) # Model Summary > Astraios-FFT is an instruction tuned model with 15.5B parameters created by finetuning StarCoderBase on CommitPackFT & OASST as described in the Astraios paper. - **Repository:** [bigcode-project/astraios](https://github.com/bigcode-project/astraios) - **Paper:** [Astraios: A Recipe for Parameter Efficient Instruction Tuning Code Language Models]() - **Languages:** 80+ Programming languages - **✨Astraios:**
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 from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "bigcode/astraios-fft" model = AutoModelForCausalLM.from_pretrained(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 ```