| Quantization made by Richard Erkhov. | |
| [Github](https://github.com/RichardErkhov) | |
| [Discord](https://discord.gg/pvy7H8DZMG) | |
| [Request more models](https://github.com/RichardErkhov/quant_request) | |
| semcoder_s_1030 - bnb 8bits | |
| - Model creator: https://huggingface.co/semcoder/ | |
| - Original model: https://huggingface.co/semcoder/semcoder_s_1030/ | |
| Original model description: | |
| --- | |
| license: other | |
| library_name: transformers | |
| license_name: deepseek | |
| license_link: https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL | |
| pipeline_tag: text-generation | |
| --- | |
| # 🤔 SemCoder: Training Code Language Models with Comprehensive Semantics Reasoning | |
| > Refer to our GitHub repo [ARiSE-Lab/SemCoder](https://github.com/ARiSE-Lab/SemCoder/) for detailed introduction to SemCoder! | |
| ## Model Details | |
| Use the code below to get started with the model. Make sure you installed the [transformers](https://huggingface.co/docs/transformers/index) library. | |
| ```python | |
| from transformers import pipeline | |
| import torch | |
| generator = pipeline( | |
| model="semcoder/semcoder_s_1030", | |
| task="text-generation", | |
| torch_dtype=torch.float16, | |
| device_map="auto", | |
| ) | |
| # Generate Code | |
| CODEGEN_REQUEST = """You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable <Code> according to <NL_Description> | |
| <NL_Description> | |
| {desc} | |
| <Code> | |
| """ | |
| desc = """You are tasked with implementing a Python class that simulates a simple version of a "To-Do List" application. The class should have the following functionalities: | |
| 1. Add a new task to the to-do list. | |
| 2. Mark a task as completed. | |
| 3. Display all tasks in the to-do list. | |
| 4. Display only the incomplete tasks in the to-do list. | |
| """ | |
| prompt = CODEGEN_REQUEST.format(desc=desc) | |
| result = generator(prompt, max_length=2048, num_return_sequences=1, temperature=0.0) | |
| code = result[0]["generated_text"].split("```python")[1].split("```")[0] | |
| print(code) | |
| # Understand Code with Monologues | |
| FWD_MNL_REQUEST = """Simulate the Execution: You are given a Python function and an assertion containing a function input. Complete the assertion containing the execution output corresponding to the given input in [ANSWER] and [/ANSWER] tags. | |
| {code} | |
| """ | |
| tests = """ | |
| todo_list = ToDoList() | |
| todo_list.add_task("Buy groceries") | |
| todo_list.add_task("Complete assignment") | |
| todo_list.mark_completed("Buy groceries") | |
| assert todo_list.tasks == ??? | |
| """ | |
| code += tests | |
| prompt = FWD_MNL_REQUEST.format(code=code) | |
| result = generator(prompt, max_length=2048, num_return_sequences=1, temperature=0.0) | |
| print(result[0]["generated_text"]) | |
| ``` | |
| ## Citation | |
| ```bibtex | |
| @article{ding2024semcoder, | |
| title={SemCoder: Training Code Language Models with Comprehensive Semantics}, | |
| author={Yangruibo Ding and Jinjun Peng and Marcus J. Min and Gail Kaiser and Junfeng Yang and Baishakhi Ray}, | |
| journal={arXiv preprint arXiv:2406.01006}, | |
| year={2024} | |
| } | |
| ``` | |
| ## Important Note | |
| SemCoder models are trained on the synthetic data generated by OpenAI models. Please pay attention to OpenAI's [terms of use](https://openai.com/policies/terms-of-use) when using the models and the datasets. SemCoder will not compete with OpenAI's commercial products. | |