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--- |
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language: |
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- en |
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pipeline_tag: text-generation |
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tags: |
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- code |
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--- |
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<h1 align="center"> OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement<h1> |
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<p align="center"> |
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<img width="1000px" alt="OpenCodeInterpreter" src="https://opencodeinterpreter.github.io/static/images/figure1.png"> |
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</p> |
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<p align="center"> |
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<a href="https://opencodeinterpreter.github.io/">[🏠Homepage]</a> |
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<a href="https://github.com/OpenCodeInterpreter/OpenCodeInterpreter/">[🛠️Code]</a> |
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</p> |
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<hr> |
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## Introduction |
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OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities. |
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## Model Usage |
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### Inference |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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model_path="OpenCodeInterpreter-CL-13B" |
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tokenizer = AutoTokenizer.from_pretrained(model_path) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_path, |
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torch_dtype=torch.bfloat16, |
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device_map="auto", |
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) |
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model.eval() |
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prompt = "Write a function to find the shared elements from the given two lists." |
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inputs = tokenizer.apply_chat_template( |
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[{'role': 'user', 'content': prompt }], |
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return_tensors="pt" |
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).to(model.device) |
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outputs = model.generate( |
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inputs, |
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max_new_tokens=1024, |
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do_sample=False, |
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pad_token_id=tokenizer.eos_token_id, |
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eos_token_id=tokenizer.eos_token_id, |
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) |
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print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)) |
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``` |
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## Contact |
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If you have any inquiries, please feel free to raise an issue or reach out to us via email at: xiangyue.work@gmail.com, zhengtianyu0428@gmail.com. |
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We're here to assist you!" |
