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README.md

@@ -1,9 +1,143 @@
+
 ---
+license: bigcode-openrail-m
+datasets:
+- bigcode/guanaco-commits
+metrics:
+- code_eval
 library_name: peft
+tags:
+- code
 ---
-## Training procedure
+# 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-LoRA 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.
+
+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-lora"
+checkpoint = "bigcode/starcoderbase"
+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
 
-### Framework versions
+- **Orchestration:** [Megatron-LM/Transformers](https://github.com/bigcode-project/octopack#training)
+- **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch)
 
+# Citation
 
-- PEFT 0.6.0.dev0
+```bibtex
+```