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README.md

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----
-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
-<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-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:**
-<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.\n\nAnswer:"
-
-**Feel free to share your generations in the Community tab!**
-
-## Generation
-```python
-# pip install -q transformers
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-checkpoint = "bigcode/astraios-1b-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.\n\nAnswer:", 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
-```