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license: apache-2.0
pipeline_tag: text-generation
base_model: Bin12345/AutoCoder_S_6.7B
---
# QuantFactory/AutoCoder_S_6.7B-GGUF
This is quantized version of [Bin12345/AutoCoder_S_6.7B](https://huggingface.co/Bin12345/AutoCoder_S_6.7B) created using llama.cpp
# Model Description
We introduced a new model designed for the Code generation task. It 33B version's 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**.
This is the 6.7B version of AutoCoder. Its base model is deepseeker-coder.
See details on the [AutoCoder GitHub](https://github.com/bin123apple/AutoCoder).
Simple test script:
```
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 |