314 lines
5.5 KiB
Markdown
314 lines
5.5 KiB
Markdown
---
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license: apache-2.0
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base_model: Qwen/Qwen2.5-1.5B-instruct # Change this to your exact Qwen base model repo
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tags:
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- text-generation
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- fine-tuned
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pipeline_tag: text-generation
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library_name: transformers
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---
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# CodeMate-Qwen
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## Model Details
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### Model Description
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CodeMate-Qwen is a coding-focused language model fine-tuned from Qwen2.5-Coder-1.5B using Low-Rank Adaptation (LoRA). The model is designed to assist developers with code generation, debugging, code explanation, refactoring, and software engineering tasks.
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The project was created to explore parameter-efficient fine-tuning techniques and build a lightweight coding assistant capable of supporting real-world development workflows.
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### Developed by
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Michael Moses
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### Funded by
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Self-funded personal research project.
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### Shared by
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Michael Moses
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### Model Type
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Causal Language Model (LLM) for Code Generation and Software Engineering Assistance.
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### Language(s)
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* English
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* Programming Languages:
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* Python
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* JavaScript
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* TypeScript
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* HTML
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* CSS
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* SQL
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* General programming concepts
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### License
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Apache 2.0 (subject to the licensing terms of the base Qwen model).
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### Finetuned From
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Qwen/Qwen2.5-Coder-1.5B
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---
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## Model Sources
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### Repository
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GitHub: https://github.com/micymike
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### Hugging Face
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https://huggingface.co/micymike
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### Demo
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Coming Soon
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---
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# Uses
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## Direct Use
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This model is intended for:
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* Code generation
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* Debugging assistance
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* Programming education
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* Code explanation
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* Refactoring recommendations
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* Developer productivity workflows
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* AI-assisted software development
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## Downstream Use
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Potential downstream applications include:
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* Coding copilots
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* Educational coding assistants
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* Automated code review systems
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* Software engineering support tools
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* Programming tutors
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## Out-of-Scope Use
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This model is not intended for:
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* Legal advice
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* Medical advice
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* Financial decision-making
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* Safety-critical systems
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* Autonomous code deployment without human review
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Generated code should always be reviewed and tested before production use.
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---
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# Bias, Risks, and Limitations
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Like all large language models, CodeMate-Qwen may:
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* Generate incorrect code
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* Produce insecure implementations
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* Hallucinate APIs or libraries
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* Miss edge cases
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* Reflect biases present in training data
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Users should validate all generated outputs before deployment.
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---
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# Recommendations
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The model performs best when:
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* Prompts are clear and specific
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* Sufficient context is provided
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* Outputs are reviewed by a developer
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The model should be considered an assistant rather than a replacement for software engineering expertise.
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---
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# How to Get Started
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model_name = "micymike/codemate-qwen-merged"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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device_map="auto"
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)
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prompt = "Write a Python function that checks if a number is prime."
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inputs = tokenizer(prompt, return_tensors="pt")
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outputs = model.generate(
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**inputs,
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max_new_tokens=256
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)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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---
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# Training Details
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## Training Data
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The training dataset consisted of instruction-response pairs focused on software engineering and programming-related tasks.
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Examples included:
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* Bug fixing
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* Code generation
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* Code explanation
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* Refactoring
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* Programming Q&A
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* Developer workflow assistance
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## Training Procedure
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The model was fine-tuned using LoRA (Low-Rank Adaptation), allowing efficient adaptation of the base model while training only a small subset of parameters.
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### Training Regime
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* Base Model: Qwen2.5-Coder-1.5B
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* Fine-Tuning Method: LoRA
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* Framework: Hugging Face Transformers
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* PEFT Library: PEFT
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* Backend: PyTorch
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---
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# Evaluation
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## Testing Data
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Evaluation was performed using programming-related prompts covering:
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* Python debugging
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* Code generation
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* Code explanation
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* Refactoring tasks
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## Metrics
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Evaluation focused primarily on qualitative assessment:
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* Instruction-following capability
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* Code correctness
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* Response quality
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* Programming relevance
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## Results
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The model demonstrated improved performance on coding-focused tasks compared to the untuned base model and showed stronger alignment with software engineering workflows.
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---
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# Environmental Impact
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### Hardware Type
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NVIDIA GPU
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### Cloud Provider
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Google Colab
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### Compute Region
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Not specified
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### Carbon Emitted
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Not measured
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---
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# Technical Specifications
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## Model Architecture
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Transformer-based autoregressive language model.
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### Base Architecture
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Qwen2.5-Coder-1.5B
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### Objective
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Next-token prediction optimized for coding and software engineering tasks.
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---
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# Compute Infrastructure
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## Hardware
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Google Colab GPU Environment
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## Software
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* Python
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* PyTorch
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* Transformers
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* PEFT
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* Hugging Face Hub
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---
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# Citation
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```bibtex
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@misc{moses2026codemateqwen,
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author = {Michael Moses},
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title = {CodeMate-Qwen: A LoRA Fine-Tuned Coding Assistant Based on Qwen2.5-Coder-1.5B},
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year = {2026},
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publisher = {Hugging Face},
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url = {https://huggingface.co/micymike}
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}
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```
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---
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# Model Card Authors
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Michael Moses
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---
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# Contact
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GitHub: https://github.com/micymike
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Email: [mosesmichael878@gmail.com](mailto:mosesmichael878@gmail.com)
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---
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# Future Work
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Planned improvements include:
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* Larger instruction datasets
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* Quantized deployments
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* Benchmark evaluation on HumanEval and MBPP
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* Additional programming language support
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* Interactive web demo
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* Advanced code review capabilities
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