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Model: bigcode/starcoder2-15b-instruct-v0.1 Source: Original Platform
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README.md
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---
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pipeline_tag: text-generation
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base_model: bigcode/starcoder2-15b
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datasets:
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- bigcode/self-oss-instruct-sc2-exec-filter-50k
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license: bigcode-openrail-m
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library_name: transformers
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tags:
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- code
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model-index:
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- name: starcoder2-15b-instruct-v0.1
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results:
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- task:
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type: text-generation
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dataset:
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name: LiveCodeBench (code generation)
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type: livecodebench-codegeneration
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metrics:
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- type: pass@1
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value: 20.4
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- task:
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type: text-generation
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dataset:
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name: LiveCodeBench (self repair)
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type: livecodebench-selfrepair
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metrics:
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- type: pass@1
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value: 20.9
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- task:
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type: text-generation
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dataset:
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name: LiveCodeBench (test output prediction)
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type: livecodebench-testoutputprediction
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metrics:
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- type: pass@1
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value: 29.8
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- task:
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type: text-generation
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dataset:
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name: LiveCodeBench (code execution)
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type: livecodebench-codeexecution
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metrics:
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- type: pass@1
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value: 28.1
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- task:
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type: text-generation
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dataset:
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name: HumanEval
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type: humaneval
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metrics:
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- type: pass@1
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value: 72.6
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- task:
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type: text-generation
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dataset:
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name: HumanEval+
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type: humanevalplus
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metrics:
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- type: pass@1
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value: 63.4
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- task:
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type: text-generation
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dataset:
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name: MBPP
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type: mbpp
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metrics:
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- type: pass@1
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value: 75.2
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- task:
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type: text-generation
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dataset:
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name: MBPP+
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type: mbppplus
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metrics:
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- type: pass@1
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value: 61.2
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- task:
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type: text-generation
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dataset:
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name: DS-1000
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type: ds-1000
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metrics:
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- type: pass@1
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value: 40.6
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---
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# StarCoder2-Instruct: Fully Transparent and Permissive Self-Alignment for Code Generation
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## Model Summary
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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.
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- **Model:** [bigcode/starcoder2-15b-instruct-v0.1](https://huggingface.co/bigcode/starcoder2-instruct-15b-v0.1)
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- **Code:** [bigcode-project/starcoder2-self-align](https://github.com/bigcode-project/starcoder2-self-align)
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- **Dataset:** [bigcode/self-oss-instruct-sc2-exec-filter-50k](https://huggingface.co/datasets/bigcode/self-oss-instruct-sc2-exec-filter-50k/)
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- **Authors:**
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[Yuxiang Wei](https://yuxiang.cs.illinois.edu),
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[Federico Cassano](https://federico.codes/),
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[Jiawei Liu](https://jw-liu.xyz),
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[Yifeng Ding](https://yifeng-ding.com),
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[Naman Jain](https://naman-ntc.github.io),
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[Harm de Vries](https://www.harmdevries.com),
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[Leandro von Werra](https://twitter.com/lvwerra),
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[Arjun Guha](https://www.khoury.northeastern.edu/home/arjunguha/main/home/),
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[Lingming Zhang](https://lingming.cs.illinois.edu).
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## Citation
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```bibtex
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@article{wei2024selfcodealign,
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title={SelfCodeAlign: Self-Alignment for Code Generation},
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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},
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year={2024},
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journal={arXiv preprint arXiv:2410.24198}
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}
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```
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## Use
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### Intended use
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The model is designed to respond to **coding-related instructions in a single turn**. Instructions in other styles may result in less accurate responses.
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Here is an example to get started with the model using the [transformers](https://huggingface.co/docs/transformers/index) library:
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```python
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import transformers
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import torch
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pipeline = transformers.pipeline(
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model="bigcode/starcoder2-15b-instruct-v0.1",
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task="text-generation",
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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def respond(instruction: str, response_prefix: str) -> str:
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messages = [{"role": "user", "content": instruction}]
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prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False)
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prompt += response_prefix
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teminators = [
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pipeline.tokenizer.eos_token_id,
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pipeline.tokenizer.convert_tokens_to_ids("###"),
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]
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result = pipeline(
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prompt,
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max_length=256,
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num_return_sequences=1,
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do_sample=False,
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eos_token_id=teminators,
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pad_token_id=pipeline.tokenizer.eos_token_id,
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truncation=True,
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)
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response = response_prefix + result[0]["generated_text"][len(prompt) :].split("###")[0].rstrip()
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return response
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instruction = "Write a quicksort function in Python with type hints and a 'less_than' parameter for custom sorting criteria."
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response_prefix = ""
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print(respond(instruction, response_prefix))
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```
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Here is the expected output:
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``````
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Here's how you can implement a quicksort function in Python with type hints and a 'less_than' parameter for custom sorting criteria:
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```python
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from typing import TypeVar, Callable
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T = TypeVar('T')
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def quicksort(items: list[T], less_than: Callable[[T, T], bool] = lambda x, y: x < y) -> list[T]:
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if len(items) <= 1:
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return items
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pivot = items[0]
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less = [x for x in items[1:] if less_than(x, pivot)]
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greater = [x for x in items[1:] if not less_than(x, pivot)]
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return quicksort(less, less_than) + [pivot] + quicksort(greater, less_than)
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```
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``````
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### Bias, Risks, and Limitations
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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.
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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).
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## Evaluation on EvalPlus, LiveCodeBench, and DS-1000
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## Training Details
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### Hyperparameters
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- **Optimizer:** Adafactor
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- **Learning rate:** 1e-5
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- **Epoch:** 4
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- **Batch size:** 64
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- **Warmup ratio:** 0.05
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- **Scheduler:** Linear
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- **Sequence length:** 1280
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- **Dropout**: Not applied
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### Hardware
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1 x NVIDIA A100 80GB
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## Resources
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- **Model:** [bigcode/starCoder2-15b-instruct-v0.1](https://huggingface.co/bigcode/starcoder2-instruct-15b-v0.1)
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- **Code:** [bigcode-project/starcoder2-self-align](https://github.com/bigcode-project/starcoder2-self-align)
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- **Dataset:** [bigcode/self-oss-instruct-sc2-exec-filter-50k](https://huggingface.co/datasets/bigcode/self-oss-instruct-sc2-exec-filter-50k/)
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### Full Data Pipeline
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Our dataset generation pipeline has several steps. We provide intermediate datasets for every step of the pipeline:
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1. Original seed dataset filtered from The Stack v1: https://huggingface.co/datasets/bigcode/python-stack-v1-functions-filtered
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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
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3. seed -> concepts: https://huggingface.co/datasets/bigcode/self-oss-instruct-sc2-concepts
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4. concepts -> instructions: https://huggingface.co/datasets/bigcode/self-oss-instruct-sc2-instructions
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5. instructions -> response: https://huggingface.co/datasets/bigcode/self-oss-instruct-sc2-responses-unfiltered
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6. Responses filtered by executing them: https://huggingface.co/datasets/bigcode/self-oss-instruct-sc2-exec-filter-500k-raw
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7. Executed responses filtered by deduplicating them (final dataset): https://huggingface.co/datasets/bigcode/self-oss-instruct-sc2-exec-filter-50k
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31
config.json
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"_name_or_path": "starcoder2-instruct-15b-v0.1",
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"architectures": [
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"Starcoder2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 0,
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"embedding_dropout": 0.0,
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"hidden_act": "gelu_pytorch_tanh",
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"hidden_size": 6144,
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"initializer_range": 0.01275,
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"intermediate_size": 24576,
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"max_position_embeddings": 16384,
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"mlp_type": "default",
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"model_type": "starcoder2",
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"norm_epsilon": 1e-05,
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"norm_type": "layer_norm",
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"num_attention_heads": 48,
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"num_hidden_layers": 40,
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"num_key_value_heads": 4,
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"residual_dropout": 0.0,
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"rope_theta": 100000,
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"sliding_window": 4096,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.39.0.dev0",
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"use_bias": true,
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"use_cache": true,
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"vocab_size": 49152
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}
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"model.layers.7.self_attn.q_proj.bias": "model-00002-of-00007.safetensors",
|
||||
"model.layers.7.self_attn.q_proj.weight": "model-00002-of-00007.safetensors",
|
||||
"model.layers.7.self_attn.v_proj.bias": "model-00002-of-00007.safetensors",
|
||||
"model.layers.7.self_attn.v_proj.weight": "model-00002-of-00007.safetensors",
|
||||
"model.layers.8.input_layernorm.bias": "model-00002-of-00007.safetensors",
|
||||
"model.layers.8.input_layernorm.weight": "model-00002-of-00007.safetensors",
|
||||
"model.layers.8.mlp.c_fc.bias": "model-00002-of-00007.safetensors",
|
||||
"model.layers.8.mlp.c_fc.weight": "model-00002-of-00007.safetensors",
|
||||
"model.layers.8.mlp.c_proj.bias": "model-00002-of-00007.safetensors",
|
||||
"model.layers.8.mlp.c_proj.weight": "model-00002-of-00007.safetensors",
|
||||
"model.layers.8.post_attention_layernorm.bias": "model-00002-of-00007.safetensors",
|
||||
"model.layers.8.post_attention_layernorm.weight": "model-00002-of-00007.safetensors",
|
||||
"model.layers.8.self_attn.k_proj.bias": "model-00002-of-00007.safetensors",
|
||||
"model.layers.8.self_attn.k_proj.weight": "model-00002-of-00007.safetensors",
|
||||
"model.layers.8.self_attn.o_proj.bias": "model-00002-of-00007.safetensors",
|
||||
"model.layers.8.self_attn.o_proj.weight": "model-00002-of-00007.safetensors",
|
||||
"model.layers.8.self_attn.q_proj.bias": "model-00002-of-00007.safetensors",
|
||||
"model.layers.8.self_attn.q_proj.weight": "model-00002-of-00007.safetensors",
|
||||
"model.layers.8.self_attn.v_proj.bias": "model-00002-of-00007.safetensors",
|
||||
"model.layers.8.self_attn.v_proj.weight": "model-00002-of-00007.safetensors",
|
||||
"model.layers.9.input_layernorm.bias": "model-00002-of-00007.safetensors",
|
||||
"model.layers.9.input_layernorm.weight": "model-00002-of-00007.safetensors",
|
||||
"model.layers.9.mlp.c_fc.bias": "model-00002-of-00007.safetensors",
|
||||
"model.layers.9.mlp.c_fc.weight": "model-00002-of-00007.safetensors",
|
||||
"model.layers.9.mlp.c_proj.bias": "model-00002-of-00007.safetensors",
|
||||
"model.layers.9.mlp.c_proj.weight": "model-00002-of-00007.safetensors",
|
||||
"model.layers.9.post_attention_layernorm.bias": "model-00002-of-00007.safetensors",
|
||||
"model.layers.9.post_attention_layernorm.weight": "model-00002-of-00007.safetensors",
|
||||
"model.layers.9.self_attn.k_proj.bias": "model-00002-of-00007.safetensors",
|
||||
"model.layers.9.self_attn.k_proj.weight": "model-00002-of-00007.safetensors",
|
||||
"model.layers.9.self_attn.o_proj.bias": "model-00002-of-00007.safetensors",
|
||||
"model.layers.9.self_attn.o_proj.weight": "model-00002-of-00007.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.bias": "model-00002-of-00007.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.weight": "model-00002-of-00007.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.bias": "model-00002-of-00007.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00002-of-00007.safetensors",
|
||||
"model.norm.bias": "model-00007-of-00007.safetensors",
|
||||
"model.norm.weight": "model-00007-of-00007.safetensors"
|
||||
}
|
||||
}
|
||||
63
special_tokens_map.json
Normal file
63
special_tokens_map.json
Normal file
@@ -0,0 +1,63 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<|endoftext|>",
|
||||
"<fim_prefix>",
|
||||
"<fim_middle>",
|
||||
"<fim_suffix>",
|
||||
"<fim_pad>",
|
||||
"<repo_name>",
|
||||
"<file_sep>",
|
||||
"<issue_start>",
|
||||
"<issue_comment>",
|
||||
"<issue_closed>",
|
||||
"<jupyter_start>",
|
||||
"<jupyter_text>",
|
||||
"<jupyter_code>",
|
||||
"<jupyter_output>",
|
||||
"<jupyter_script>",
|
||||
"<empty_output>",
|
||||
"<code_to_intermediate>",
|
||||
"<intermediate_to_code>",
|
||||
"<pr>",
|
||||
"<pr_status>",
|
||||
"<pr_is_merged>",
|
||||
"<pr_base>",
|
||||
"<pr_file>",
|
||||
"<pr_base_code>",
|
||||
"<pr_diff>",
|
||||
"<pr_diff_hunk>",
|
||||
"<pr_comment>",
|
||||
"<pr_event_id>",
|
||||
"<pr_review>",
|
||||
"<pr_review_state>",
|
||||
"<pr_review_comment>",
|
||||
"<pr_in_reply_to_review_id>",
|
||||
"<pr_in_reply_to_comment_id>",
|
||||
"<pr_diff_hunk_comment_line>",
|
||||
"<NAME>",
|
||||
"<EMAIL>",
|
||||
"<KEY>",
|
||||
"<PASSWORD>"
|
||||
],
|
||||
"bos_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"unk_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
98409
tokenizer.json
Normal file
98409
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
357
tokenizer_config.json
Normal file
357
tokenizer_config.json
Normal file
@@ -0,0 +1,357 @@
|
||||
{
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"0": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"1": {
|
||||
"content": "<fim_prefix>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"2": {
|
||||
"content": "<fim_middle>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"3": {
|
||||
"content": "<fim_suffix>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"4": {
|
||||
"content": "<fim_pad>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"5": {
|
||||
"content": "<repo_name>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"6": {
|
||||
"content": "<file_sep>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"7": {
|
||||
"content": "<issue_start>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"8": {
|
||||
"content": "<issue_comment>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"9": {
|
||||
"content": "<issue_closed>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"10": {
|
||||
"content": "<jupyter_start>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"11": {
|
||||
"content": "<jupyter_text>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"12": {
|
||||
"content": "<jupyter_code>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"13": {
|
||||
"content": "<jupyter_output>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"14": {
|
||||
"content": "<jupyter_script>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"15": {
|
||||
"content": "<empty_output>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"16": {
|
||||
"content": "<code_to_intermediate>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"17": {
|
||||
"content": "<intermediate_to_code>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"18": {
|
||||
"content": "<pr>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"19": {
|
||||
"content": "<pr_status>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"20": {
|
||||
"content": "<pr_is_merged>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"21": {
|
||||
"content": "<pr_base>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"22": {
|
||||
"content": "<pr_file>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"23": {
|
||||
"content": "<pr_base_code>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"24": {
|
||||
"content": "<pr_diff>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"25": {
|
||||
"content": "<pr_diff_hunk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"26": {
|
||||
"content": "<pr_comment>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"27": {
|
||||
"content": "<pr_event_id>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"28": {
|
||||
"content": "<pr_review>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"29": {
|
||||
"content": "<pr_review_state>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"30": {
|
||||
"content": "<pr_review_comment>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"31": {
|
||||
"content": "<pr_in_reply_to_review_id>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32": {
|
||||
"content": "<pr_in_reply_to_comment_id>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"33": {
|
||||
"content": "<pr_diff_hunk_comment_line>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"34": {
|
||||
"content": "<NAME>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"35": {
|
||||
"content": "<EMAIL>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"36": {
|
||||
"content": "<KEY>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"37": {
|
||||
"content": "<PASSWORD>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|endoftext|>",
|
||||
"<fim_prefix>",
|
||||
"<fim_middle>",
|
||||
"<fim_suffix>",
|
||||
"<fim_pad>",
|
||||
"<repo_name>",
|
||||
"<file_sep>",
|
||||
"<issue_start>",
|
||||
"<issue_comment>",
|
||||
"<issue_closed>",
|
||||
"<jupyter_start>",
|
||||
"<jupyter_text>",
|
||||
"<jupyter_code>",
|
||||
"<jupyter_output>",
|
||||
"<jupyter_script>",
|
||||
"<empty_output>",
|
||||
"<code_to_intermediate>",
|
||||
"<intermediate_to_code>",
|
||||
"<pr>",
|
||||
"<pr_status>",
|
||||
"<pr_is_merged>",
|
||||
"<pr_base>",
|
||||
"<pr_file>",
|
||||
"<pr_base_code>",
|
||||
"<pr_diff>",
|
||||
"<pr_diff_hunk>",
|
||||
"<pr_comment>",
|
||||
"<pr_event_id>",
|
||||
"<pr_review>",
|
||||
"<pr_review_state>",
|
||||
"<pr_review_comment>",
|
||||
"<pr_in_reply_to_review_id>",
|
||||
"<pr_in_reply_to_comment_id>",
|
||||
"<pr_diff_hunk_comment_line>",
|
||||
"<NAME>",
|
||||
"<EMAIL>",
|
||||
"<KEY>",
|
||||
"<PASSWORD>"
|
||||
],
|
||||
"bos_token": "<|endoftext|>",
|
||||
"chat_template": "{{bos_token}}{{'You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions.\n\n'}}\n{%- for message in messages %}\n {%- if message['role'] == 'system' %}\n {{ raise_exception('System messages are not allowed in this template.') }}\n {%- else %}\n {%- if message['role'] == 'user' %}\n{{'### Instruction\n' + message['content'] + '\n\n'}}\n {%- else %}\n{{'### Response\n' + message['content'] + eos_token + '\n\n'}}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{{'### Response\n'}}",
|
||||
"clean_up_tokenization_spaces": true,
|
||||
"eos_token": "<|endoftext|>",
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"tokenizer_class": "GPT2Tokenizer",
|
||||
"unk_token": "<|endoftext|>",
|
||||
"vocab_size": 49152
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user