Starcoder2 15b Instruct V0.1

🚀 StarCoder2-Instruct: Fully Transparent and Permissive Self-Alignment for Code Generation

StarCoder2-Instruct is the first entirely self-aligned code Large Language Model (LLM) trained with a fully permissive and transparent pipeline, enabling high - quality code generation.

✨ Features

• The very first entirely self - aligned code Large Language Model (LLM) trained with a fully permissive and transparent pipeline.
• The open - source pipeline uses StarCoder2 - 15B to generate instruction - response pairs for fine - tuning without human annotations or distilled data from huge and proprietary LLMs.

📦 Installation

The README does not provide specific installation steps, so this section is skipped.

💻 Usage Examples

Basic Usage

import transformers
import torch

pipeline = transformers.pipeline(
    model="bigcode/starcoder2-15b-instruct-v0.1",
    task="text-generation",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

def respond(instruction: str, response_prefix: str) -> str:
    messages = [{"role": "user", "content": instruction}]
    prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False)
    prompt += response_prefix

    teminators = [
        pipeline.tokenizer.eos_token_id,
        pipeline.tokenizer.convert_tokens_to_ids("###"),
    ]

    result = pipeline(
        prompt,
        max_length=256,
        num_return_sequences=1,
        do_sample=False,
        eos_token_id=teminators,
        pad_token_id=pipeline.tokenizer.eos_token_id,
        truncation=True,
    )
    response = response_prefix + result[0]["generated_text"][len(prompt) :].split("###")[0].rstrip()
    return response


instruction = "Write a quicksort function in Python with type hints and a 'less_than' parameter for custom sorting criteria."
response_prefix = ""

print(respond(instruction, response_prefix))

Expected Output

Here's how you can implement a quicksort function in Python with type hints and a 'less_than' parameter for custom sorting criteria:

```python
from typing import TypeVar, Callable

T = TypeVar('T')

def quicksort(items: list[T], less_than: Callable[[T, T], bool] = lambda x, y: x < y) -> list[T]:
    if len(items) <= 1:
        return items

    pivot = items[0]
    less = [x for x in items[1:] if less_than(x, pivot)]
    greater = [x for x in items[1:] if not less_than(x, pivot)]
    return quicksort(less, less_than) + [pivot] + quicksort(greater, less_than)

## 📚 Documentation
### Model Summary
We introduce StarCoder2 - 15B - Instruct - v0.1, the very first entirely self - aligned code Large Language Model (LLM) trained with a fully permissive and transparent pipeline. Our open - source pipeline uses StarCoder2 - 15B to generate thousands of instruction - response pairs, which are then used to fine - tune StarCoder - 15B itself without any human annotations or distilled data from huge and proprietary LLMs.

- **Model:** [bigcode/starcoder2-15b-instruct-v0.1](https://huggingface.co/bigcode/starcoder2-instruct-15b-v0.1)
- **Code:** [bigcode-project/starcoder2-self-align](https://github.com/bigcode-project/starcoder2-self-align)
- **Dataset:** [bigcode/self-oss-instruct-sc2-exec-filter-50k](https://huggingface.co/datasets/bigcode/self-oss-instruct-sc2-exec-filter-50k/)
- **Authors:**
[Yuxiang Wei](https://yuxiang.cs.illinois.edu),
[Federico Cassano](https://federico.codes/),
[Jiawei Liu](https://jw-liu.xyz),
[Yifeng Ding](https://yifeng-ding.com),
[Naman Jain](https://naman-ntc.github.io),
[Harm de Vries](https://www.harmdevries.com),
[Leandro von Werra](https://twitter.com/lvwerra),
[Arjun Guha](https://www.khoury.northeastern.edu/home/arjunguha/main/home/),
[Lingming Zhang](https://lingming.cs.illinois.edu).

### Intended use
The model is designed to respond to **coding - related instructions in a single turn**. Instructions in other styles may result in less accurate responses.

### Bias, Risks, and Limitations
StarCoder2 - 15B - Instruct - v0.1 is primarily finetuned for Python code generation tasks that can be verified through execution, which may lead to certain biases and limitations. For example, the model might not adhere strictly to instructions that dictate the output format. In these situations, it's beneficial to provide a **response prefix** or a **one - shot example** to steer the model’s output. Additionally, the model may have limitations with other programming languages and out - of - domain coding tasks.

The model also inherits the bias, risks, and limitations from its base StarCoder2 - 15B model. For more information, please refer to the [StarCoder2 - 15B model card](https://huggingface.co/bigcode/starcoder2-15b).

### Evaluation on EvalPlus, LiveCodeBench, and DS - 1000
![EvalPlus](https://huggingface.co/datasets/bigcode/starcoder2-instruct-assets/resolve/main/evalplus.png)
![LiveCodeBench and DS - 1000](https://huggingface.co/datasets/bigcode/starcoder2-instruct-assets/resolve/main/lcb-ds1000.png)

### Training Details
#### Hyperparameters
| Property | Details |
|----------|---------|
| Optimizer | Adafactor |
| Learning rate | 1e - 5 |
| Epoch | 4 |
| Batch size | 64 |
| Warmup ratio | 0.05 |
| Scheduler | Linear |
| Sequence length | 1280 |
| Dropout | Not applied |

#### Hardware
1 x NVIDIA A100 80GB

### Resources
- **Model:** [bigcode/starCoder2-15b-instruct-v0.1](https://huggingface.co/bigcode/starcoder2-instruct-15b-v0.1)
- **Code:** [bigcode-project/starcoder2-self-align](https://github.com/bigcode-project/starcoder2-self-align)
- **Dataset:** [bigcode/self-oss-instruct-sc2-exec-filter-50k](https://huggingface.co/datasets/bigcode/self-oss-instruct-sc2-exec-filter-50k/)

### Full Data Pipeline
Our dataset generation pipeline has several steps. We provide intermediate datasets for every step of the pipeline:
1. Original seed dataset filtered from The Stack v1: https://huggingface.co/datasets/bigcode/python-stack-v1-functions-filtered
2. Seed dataset filtered using StarCoder2 - 15B as a judge for removing items with bad docstrings: https://huggingface.co/datasets/bigcode/python-stack-v1-functions-filtered-sc2
3. seed -> concepts: https://huggingface.co/datasets/bigcode/self-oss-instruct-sc2-concepts
4. concepts -> instructions: https://huggingface.co/datasets/bigcode/self-oss-instruct-sc2-instructions
5. instructions -> response: https://huggingface.co/datasets/bigcode/self-oss-instruct-sc2-responses-unfiltered
6. Responses filtered by executing them: https://huggingface.co/datasets/bigcode/self-oss-instruct-sc2-exec-filter-500k-raw
7. Executed responses filtered by deduplicating them (final dataset): https://huggingface.co/datasets/bigcode/self-oss-instruct-sc2-exec-filter-50k

## 🔧 Technical Details
The README does not provide detailed technical implementation information, so this section is skipped.

## 📄 License
The model is licensed under [bigcode-openrail-m](https://huggingface.co/bigcode/starcoder2-instruct-15b-v0.1/blob/main/LICENSE.txt).

## 📖 Citation
```bibtex
@article{wei2024selfcodealign,
  title={SelfCodeAlign: Self-Alignment for Code Generation}, 
  author={Yuxiang Wei and Federico Cassano and Jiawei Liu and Yifeng Ding and Naman Jain and Zachary Mueller and Harm de Vries and Leandro von Werra and Arjun Guha and Lingming Zhang},
  year={2024},
  journal={arXiv preprint arXiv:2410.24198}
}