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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/commitpackft |
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- bigcode/oasst-octopack |
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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: OctoCoder |
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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: bigcode/humanevalpack |
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name: HumanEvalSynthesize Python |
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metrics: |
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- name: pass@1 |
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type: pass@1 |
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value: 46.2 |
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verified: false |
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- task: |
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type: text-generation |
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dataset: |
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type: bigcode/humanevalpack |
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name: HumanEvalSynthesize JavaScript |
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metrics: |
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- name: pass@1 |
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type: pass@1 |
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value: 39.2 |
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verified: false |
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--- |
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# OctoCoder |
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Play with the model on the [TODO Playground](https://huggingface.co/spaces/bigcode/bigcode-playground). |
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## Table of Contents |
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1. [Model Summary](##model-summary) |
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2. [Use](##use) |
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3. [Limitations](##limitations) |
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4. [Training](##training) |
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5. [License](##license) |
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6. [Citation](##citation) |
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## Model Summary |
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OctoCoder is an instruction tuned model with 15.5B parameters created by finetuning StarCoder on CommitPackFT & OASST as described in the OctoPack paper. |
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- **Repository:** [bigcode/octopack](https://github.com/bigcode-project/octopack) |
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- **Paper:** [TODO]() |
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- **Languages:** 80+ Programming languages |
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## Use |
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### Intended use |
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The model follows instructions provided in the input. We recommend prefacing your input with "Question: " and finishing with "Answer:", for example: "Question: Please write a function in Python that performs bubble sort.\n\nAnswer:" |
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**Feel free to share your generations in the Community tab!** |
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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/octocoder" |
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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("Question: Please write a function in Python that performs bubble sort.\n\nAnswer:", 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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- **Steps:** 250k pretraining & 30 instruction tuning |
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- **Pretraining tokens:** 1 trillion pretraining & 2M instruction tuning |
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- **Precision:** bfloat16 |
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## Hardware |
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- **Pretraining:** |
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- **GPUs:** 512 Tesla A100 |
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- **Training time:** 24 days |
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- **Instruction tuning:** |
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- **GPUs:** 8 Tesla A100 |
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- **Training time:** 4 hours |
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## Software |
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- **Orchestration:** [Megatron-LM/Transformers](https://github.com/bigcode-project/octopack#training) |
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- **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch) |
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# Citation |
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TODO |
