README.md

metadata

pipeline_tag: text-generation
base_model: bigcode/starcoder2-15b
datasets:
  - bigcode/self-oss-instruct-sc2-exec-filter-50k
license: bigcode-openrail-m
library_name: transformers
tags:
  - code
model-index:
  - name: starcoder2-15b-instruct-v0.1
    results:
      - task:
          type: text-generation
        dataset:
          name: LiveCodeBench (code generation)
          type: livecodebench-codegeneration
        metrics:
          - type: pass@1
            value: 20.4
      - task:
          type: text-generation
        dataset:
          name: LiveCodeBench (self repair)
          type: livecodebench-selfrepair
        metrics:
          - type: pass@1
            value: 20.9
      - task:
          type: text-generation
        dataset:
          name: LiveCodeBench (test output prediction)
          type: livecodebench-testoutputprediction
        metrics:
          - type: pass@1
            value: 29.8
      - task:
          type: text-generation
        dataset:
          name: LiveCodeBench (code execution)
          type: livecodebench-codeexecution
        metrics:
          - type: pass@1
            value: 28.1
      - task:
          type: text-generation
        dataset:
          name: HumanEval
          type: humaneval
        metrics:
          - type: pass@1
            value: 72.6
      - task:
          type: text-generation
        dataset:
          name: HumanEval+
          type: humanevalplus
        metrics:
          - type: pass@1
            value: 63.4
      - task:
          type: text-generation
        dataset:
          name: MBPP
          type: mbpp
        metrics:
          - type: pass@1
            value: 75.2
      - task:
          type: text-generation
        dataset:
          name: MBPP+
          type: mbppplus
        metrics:
          - type: pass@1
            value: 61.2
      - task:
          type: text-generation
        dataset:
          name: DS-1000
          type: ds-1000
        metrics:
          - type: pass@1
            value: 40.6

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

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
• Code: bigcode-project/starcoder2-self-align
• Dataset: bigcode/self-oss-instruct-sc2-exec-filter-50k
• Authors: Yuxiang Wei, Federico Cassano, Jiawei Liu, Yifeng Ding, Naman Jain, Harm de Vries, Leandro von Werra, Arjun Guha, Lingming Zhang.

Use

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.

Here is an example to get started with the model using the transformers library:

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))

Here is the 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)
```

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.

Evaluation on EvalPlus, LiveCodeBench, and DS-1000

Training Details

Hyperparameters

• 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
• Code: bigcode-project/starcoder2-self-align
• Dataset: bigcode/self-oss-instruct-sc2-exec-filter-50k