|
--- |
|
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 |
