File size: 4,984 Bytes
93adc38 72ae17b 93adc38 72ae17b 93adc38 734a5f4 93adc38 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 |
---
license: bigcode-openrail-m
datasets:
- bigcode/guanaco-commits
metrics:
- code_eval
library_name: peft
tags:
- code
---
# Astraios: Parameter-Efficient Instruction Tuning Code Large Language Models
<p align="center" width="100%">
<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>
</p>
# Table of Contents
1. [Model Summary](#model-summary)
2. [Use](#use)
3. [Training](#training)
4. [Citation](#citation)
# Model Summary
> Astraios-1B-P-Tuning 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: Parameter-Efficient Instruction Tuning Code Large Language Models]()
- **Languages:** 80+ Programming languages
- **✨Astraios:**
<table>
<tr>
<th>Data</t>
<td><a href=https://huggingface.co/datasets/bigcode/guanaco-commits>CommitPackFT+OASST</a></td>
<td>Filtered version of CommitPack and OASST for high-quality commit messages that resemble instructions</td>
</tr>
<tr>
<th>Model</t>
<td><a href=https://huggingface.co/collections/bigcode/astraios-1b-6576ff1b8e449026ae327c1c>Astraios-1B</a></td>
<td>Collection of StarCoderBase-1B models instruction tuned on CommitPackFT + OASST with different tuning methods</td>
</tr>
<tr>
<th></t>
<td><a href=https://huggingface.co/collections/bigcode/astraios-3b-6577127317ee44ff547252d3>Astraios-3B</a></td>
<td>Collection of StarCoderBase-3B (3B parameters) models instruction tuned on CommitPackFT + OASST with different tuning methods</td>
</tr>
<tr>
<th></t>
<td><a href=https://huggingface.co/collections/starpeft/starcoderbase-7b-650c1f028b45cfec8e72c265>Astraios-7B</a></td>
<td>Collection of StarCoderBase-7B (7B parameters) models instruction tuned on CommitPackFT + OASST with different tuning methods</td>
</tr>
<tr>
<th></t>
<td><a href=https://huggingface.co/collections/bigcode/astraios-16b-65788b7476b6de79781054cc>Astraios-16B</a></td>
<td>Collection of StarCoderBase-16B (16B parameters) models instruction tuned on CommitPackFT + OASST with different tuning methods</td>
</tr>
<tr>
<th>Evaluation</t>
<td><a href=https://huggingface.co/datasets/code_x_glue_cc_clone_detection_big_clone_bench>BigCloneBench</a></td>
<td>Dataset for clone detection; We use 2,000 samples for evaluation</td>
</tr>
<tr>
<th></t>
<td><a href=https://huggingface.co/datasets/code_x_glue_cc_defect_detection>Devign</a></td>
<td>Dataset for defect detection; We use 2,000 samples for evaluation</td>
</tr>
<tr>
<th></t>
<td><a href=https://huggingface.co/datasets/bigcode/humanevalpack>HumanEvalPack</a></td>
<td>Extension of OpenAI's HumanEval to cover 3 scenarios across 6 languages</td>
</tr>
<tr>
<th></t>
<td><a href=https://huggingface.co/datasets/RaymondLi/perturbed_humaneval>ReCode</a></td>
<td>Dataset for the robustness of code generation, covering 4 variants</td>
</tr>
<tr>
<th></t>
<td><a href=https://huggingface.co/datasets/moyix/asleep_keyboard>Asleep At The Keyboard</a></td>
<td>Datasets for security of code generation; We use DoW for evaluation</td>
</tr>
</table>
# 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
```
|