| Quantization made by Richard Erkhov. |
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| [Github](https://github.com/RichardErkhov) |
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| [Discord](https://discord.gg/pvy7H8DZMG) |
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| [Request more models](https://github.com/RichardErkhov/quant_request) |
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| semcoder_1030 - bnb 8bits |
| - Model creator: https://huggingface.co/semcoder/ |
| - Original model: https://huggingface.co/semcoder/semcoder_1030/ |
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| Original model description: |
| ---
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| license: other
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| library_name: transformers
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| license_name: deepseek
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| license_link: https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL
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| pipeline_tag: text-generation
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| ---
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| # 🤔 SemCoder: Training Code Language Models with Comprehensive Semantics Reasoning
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| > Refer to our GitHub repo [ARiSE-Lab/SemCoder](https://github.com/ARiSE-Lab/SemCoder/) for detailed introduction to SemCoder!
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| ## Model Details
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| Use the code below to get started with the model. Make sure you installed the [transformers](https://huggingface.co/docs/transformers/index) library.
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| ```python
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| from transformers import pipeline
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| import torch
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| generator = pipeline(
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| model="semcoder/semcoder_1030",
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| task="text-generation",
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| torch_dtype=torch.float16,
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| device_map="auto",
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| )
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| # Generate Code
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| CODEGEN_REQUEST = """You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable <Code> according to <NL_Description>
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| <NL_Description>
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| {desc}
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| <Code>
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| """
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| 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:
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| 1. Add a new task to the to-do list.
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| 2. Mark a task as completed.
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| 3. Display all tasks in the to-do list.
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| 4. Display only the incomplete tasks in the to-do list.
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| """
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| prompt = CODEGEN_REQUEST.format(desc=desc)
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| result = generator(prompt, max_length=2048, num_return_sequences=1, temperature=0.0)
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| code = result[0]["generated_text"].split("```python")[1].split("```")[0]
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| print(code)
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| # Understand Code with Monologues
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| 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.
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| {code}
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| """
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| tests = """
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| todo_list = ToDoList()
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| todo_list.add_task("Buy groceries")
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| todo_list.add_task("Complete assignment")
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| todo_list.mark_completed("Buy groceries")
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| assert todo_list.tasks == ???
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| """
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| code += tests
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| prompt = FWD_MNL_REQUEST.format(code=code)
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| result = generator(prompt, max_length=2048, num_return_sequences=1, temperature=0.0)
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| print(result[0]["generated_text"])
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| ```
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| ## Citation
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| ```bibtex
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| @article{ding2024semcoder,
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| title={SemCoder: Training Code Language Models with Comprehensive Semantics},
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| author={Yangruibo Ding and Jinjun Peng and Marcus J. Min and Gail Kaiser and Junfeng Yang and Baishakhi Ray},
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| journal={arXiv preprint arXiv:2406.01006},
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| year={2024}
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| }
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| ```
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| ## Important Note
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| 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. |
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