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license: apache-2.0 |
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We introduced a new model designed for the Code generation task. Its test accuracy on the HumanEval base dataset surpasses that of GPT-4 Turbo (April 2024). (90.9% vs 90.2%). |
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Additionally, compared to previous open-source models, AutoCoder offers a new feature: it can **automatically install the required packages** and attempt to run the code until it deems there are no issues, **whenever the user wishes to execute the code**. |
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See details on the [AutoCoder GitHub](https://github.com/bin123apple/AutoCoder). |
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Simple test script: |
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``` |
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model_path = "" |
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tokenizer = AutoTokenizer.from_pretrained(model_path) |
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model = AutoModelForCausalLM.from_pretrained(model_path, |
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device_map="auto") |
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HumanEval = load_dataset("evalplus/humanevalplus") |
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Input = "" # input your question here |
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messages=[ |
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{ 'role': 'user', 'content': Input} |
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] |
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inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, |
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return_tensors="pt").to(model.device) |
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outputs = model.generate(inputs, |
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max_new_tokens=1024, |
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do_sample=False, |
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temperature=0.0, |
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top_p=1.0, |
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num_return_sequences=1, |
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eos_token_id=tokenizer.eos_token_id) |
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answer = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True) |
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``` |
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Paper: https://arxiv.org/abs/2405.14906 |
