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--- |
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license: bigcode-openrail-m |
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datasets: |
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- bigcode/guanaco-commits |
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metrics: |
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- code_eval |
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library_name: peft |
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tags: |
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- code |
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--- |
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# Astraios: A Recipe for Parameter-Efficient Instruction Tuning Code Language Models |
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<p align="center" width="100%"> |
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<a ><img src="https://github.com/bigcode-project/astraios/blob/main/visuals/banner.png?raw=true" alt="Astraios" style="width: 20%; min-width: 300px; display: block; margin: auto;"></a> |
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</p> |
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# Table of Contents |
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1. [Model Summary](#model-summary) |
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2. [Use](#use) |
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3. [Training](#training) |
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4. [Citation](#citation) |
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# Model Summary |
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> Astraios-3B-IA3 is an instruction tuned model with 15.5B parameters created by finetuning StarCoderBase on CommitPackFT & OASST as described in the Astraios paper. |
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- **Repository:** [bigcode-project/astraios](https://github.com/bigcode-project/astraios) |
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- **Paper:** [Astraios: A Recipe for Parameter Efficient Instruction Tuning Code Language Models]() |
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- **Languages:** 80+ Programming languages |
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- **✨Astraios:** |
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<table> |
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<tr> |
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<th>Data</t> |
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<td><a href=https://huggingface.co/datasets/bigcode/guanaco-commits>CommitPackFT+OASST</a></td> |
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<td>Filtered version of CommitPack and OASST for high-quality commit messages that resemble instructions</td> |
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</tr> |
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<tr> |
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<th>Model</t> |
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<td><a href=https://huggingface.co/collections/bigcode/astraios-1b-6576ff1b8e449026ae327c1c>Astraios-1B</a></td> |
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<td>Collection of StarCoderBase-1B models instruction tuned on CommitPackFT + OASST with different tuning methods</td> |
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</tr> |
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<tr> |
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<th></t> |
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<td><a href=https://huggingface.co/collections/bigcode/astraios-3b-6577127317ee44ff547252d3>Astraios-3B</a></td> |
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<td>Collection of StarCoderBase-3B (3B parameters) models instruction tuned on CommitPackFT + OASST with different tuning methods</td> |
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</tr> |
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<tr> |
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<th></t> |
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<td><a href=https://huggingface.co/collections/starpeft/starcoderbase-7b-650c1f028b45cfec8e72c265>Astraios-7B</a></td> |
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<td>Collection of StarCoderBase-7B (7B parameters) models instruction tuned on CommitPackFT + OASST with different tuning methods</td> |
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</tr> |
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<tr> |
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<th></t> |
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<td><a href=https://huggingface.co/collections/bigcode/astraios-16b-65788b7476b6de79781054cc>Astraios-16B</a></td> |
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<td>Collection of StarCoderBase-16B (16B parameters) models instruction tuned on CommitPackFT + OASST with different tuning methods</td> |
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</tr> |
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<tr> |
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<th>Evaluation</t> |
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<td><a href=https://huggingface.co/datasets/code_x_glue_cc_clone_detection_big_clone_bench>BigCloneBench</a></td> |
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<td>Dataset for clone detection; We use 2,000 samples for evaluation</td> |
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</tr> |
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<tr> |
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<th></t> |
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<td><a href=https://huggingface.co/datasets/code_x_glue_cc_defect_detection>Devign</a></td> |
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<td>Dataset for defect detection; We use 2,000 samples for evaluation</td> |
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</tr> |
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<tr> |
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<th></t> |
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<td><a href=https://huggingface.co/datasets/bigcode/humanevalpack>HumanEvalPack</a></td> |
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<td>Extension of OpenAI's HumanEval to cover 3 scenarios across 6 languages</td> |
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</tr> |
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<tr> |
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<th></t> |
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<td><a href=https://huggingface.co/datasets/RaymondLi/perturbed_humaneval>ReCode</a></td> |
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<td>Dataset for the robustness of code generation, covering 4 variants</td> |
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</tr> |
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<tr> |
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<th></t> |
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<td><a href=https://huggingface.co/datasets/moyix/asleep_keyboard>Asleep At The Keyboard</a></td> |
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<td>Datasets for security of code generation; We use DoW for evaluation</td> |
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</tr> |
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</table> |
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# Use |
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## Intended use |
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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. |
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Answer:" |
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**Feel free to share your generations in the Community tab!** |
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## Generation |
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```python |
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# pip install -q transformers |
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# pip install -e git+https://github.com/bigcode-project/astraios#subdirectory=peft |
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from peft import PeftModel |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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peft_checkpoint = "bigcode/astraios-3b-ia3" |
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checkpoint = "bigcode/starcoderbase-3b" |
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model = AutoModelForCausalLM.from_pretrained(checkpoint) |
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model = PeftModel.from_pretrained(model, peft_checkpoint) |
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device = "cuda" # for GPU usage or "cpu" for CPU usage |
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tokenizer = AutoTokenizer.from_pretrained(checkpoint) |
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model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device) |
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inputs = tokenizer.encode("Question: Please write a function in Python that performs bubble sort. |
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Answer:", return_tensors="pt").to(device) |
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outputs = model.generate(inputs) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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# Training |
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## Model |
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- **Architecture:** GPT-2 model with multi-query attention and Fill-in-the-Middle objective |
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- **Steps:** 250k pretraining & 200 instruction tuning |
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- **Precision:** fp32 |
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## Hardware |
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- **Pretraining:** |
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- **GPUs:** 512 Tesla A100 |
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- **Training time:** 24 days |
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- **Instruction tuning:** |
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- **GPUs:** 8 Tesla A100 |
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## Software |
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- **Orchestration:** [Megatron-LM/Transformers](https://github.com/bigcode-project/octopack#training) |
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- **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch) |
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# Citation |
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```bibtex |
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``` |
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