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README.md

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+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.
+