| | --- |
| | license: apache-2.0 |
| | --- |
| | |
| | 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%). |
| |
|
| | 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**. |
| |
|
| | Its base model is deepseeker-coder. |
| |
|
| | See details on the [AutoCoder GitHub](https://github.com/bin123apple/AutoCoder). |
| |
|
| | Simple test script: |
| |
|
| | ```python |
| | from transformers import AutoTokenizer, AutoModelForCausalLM |
| | from datasets import load_dataset |
| | |
| | model_path = "" |
| | tokenizer = AutoTokenizer.from_pretrained(model_path) |
| | model = AutoModelForCausalLM.from_pretrained(model_path, |
| | device_map="auto") |
| | |
| | HumanEval = load_dataset("evalplus/humanevalplus") |
| | |
| | Input = "" # input your question here |
| | |
| | messages=[ |
| | { 'role': 'user', 'content': Input} |
| | ] |
| | inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, |
| | return_tensors="pt").to(model.device) |
| | |
| | outputs = model.generate(inputs, |
| | max_new_tokens=1024, |
| | do_sample=False, |
| | temperature=0.0, |
| | top_p=1.0, |
| | num_return_sequences=1, |
| | eos_token_id=tokenizer.eos_token_id) |
| | |
| | answer = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True) |
| | ``` |
| |
|
| | Paper: https://arxiv.org/abs/2405.14906 |
