| | ---
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| | license: apache-2.0
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| | ---
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| |
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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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| |
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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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| |
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| | See details on the [AutoCoder GitHub](https://github.com/bin123apple/AutoCoder).
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| |
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| | Simple test script:
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| |
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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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| |
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| | HumanEval = load_dataset("evalplus/humanevalplus")
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| |
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| | Input = "" # input your question here
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| |
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| |
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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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| |
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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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| |
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| | answer = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)
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| |
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| |
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| | ``` |
