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--- |
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pipeline_tag: text-generation |
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inference: true |
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widget: |
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- text: 'def print_hello_world():' |
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example_title: Hello world |
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group: Python |
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license: bigcode-openrail-m |
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datasets: |
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- bigcode/the-stack-dedup |
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metrics: |
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- code_eval |
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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: Tiny-StarCoder-Py |
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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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type: openai_humaneval |
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name: HumanEval |
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metrics: |
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- name: pass@1 |
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type: pass@1 |
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value: 7.84% |
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verified: false |
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--- |
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# TinyStarCoderPy |
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This is a 164M parameters model with the same architecture as [StarCoder](https://huggingface.co/bigcode/starcoder) (8k context length, MQA & FIM). It was trained on the Python data from [StarCoderData](https://huggingface.co/datasets/bigcode/starcoderdata) |
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for ~6 epochs which amounts to 100B tokens. |
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## Use |
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### Intended use |
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The model was trained on GitHub code, to assist with some tasks like [Assisted Generation](https://huggingface.co/blog/assisted-generation). For pure code completion, we advise using our 15B models [StarCoder]() or [StarCoderBase](). |
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### Generation |
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```python |
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# pip install -q transformers |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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checkpoint = "bigcode/tiny_starcoder_py" |
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device = "cuda" # for GPU usage or "cpu" for CPU usage |
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tokenizer = AutoTokenizer.from_pretrained(checkpoint) |
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model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device) |
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inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to(device) |
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outputs = model.generate(inputs) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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### Fill-in-the-middle |
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Fill-in-the-middle uses special tokens to identify the prefix/middle/suffix part of the input and output: |
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```python |
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input_text = "<fim_prefix>def print_one_two_three():\n print('one')\n <fim_suffix>\n print('three')<fim_middle>" |
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inputs = tokenizer.encode(input_text, return_tensors="pt").to(device) |
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outputs = model.generate(inputs) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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# Training |
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## Model |
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- **Architecture:** GPT-2 model with multi-query attention and Fill-in-the-Middle objective |
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- **Pretraining steps:** 50k |
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- **Pretraining tokens:** 100 billion |
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- **Precision:** bfloat16 |
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## Hardware |
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- **GPUs:** 32 Tesla A100 |
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- **Training time:** 18 hours |
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## Software |
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- **Orchestration:** [Megatron-LM](https://github.com/bigcode-project/Megatron-LM) |
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- **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch) |
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- **BP16 if applicable:** [apex](https://github.com/NVIDIA/apex) |
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# License |
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The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement [here](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement). |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_bigcode__tiny_starcoder_py) |
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| Metric | Value | |
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|-----------------------|---------------------------| |
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| Avg. | 25.43 | |
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| ARC (25-shot) | 20.99 | |
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| HellaSwag (10-shot) | 28.77 | |
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| MMLU (5-shot) | 26.79 | |
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| TruthfulQA (0-shot) | 47.68 | |
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| Winogrande (5-shot) | 51.22 | |
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| GSM8K (5-shot) | 0.99 | |
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| DROP (3-shot) | 1.57 | |
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