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Model: micymike/codemate-qwen-1.5B Source: Original Platform
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313
README.md
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README.md
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---
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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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54
chat_template.jinja
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54
chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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61
config.json
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"dtype": "bfloat16",
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"eos_token_id": 151643,
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"hidden_act": "silu",
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"hidden_size": 1536,
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"initializer_range": 0.02,
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"intermediate_size": 8960,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
|
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 32768,
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"max_window_layers": 28,
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"model_type": "qwen2",
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"num_attention_heads": 12,
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"num_hidden_layers": 28,
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"num_key_value_heads": 2,
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"pad_token_id": null,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 1000000.0,
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": true,
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"transformers_version": "5.10.2",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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generation_config.json
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generation_config.json
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{
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"bos_token_id": 151643,
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"eos_token_id": 151643,
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"max_new_tokens": 2048,
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"transformers_version": "5.10.2"
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}
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3
model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:e24fd67c9cd65ba72686bc57383ee40cc13361d66fe79adef8b4cc9606107b0e
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size 3087467144
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3
tokenizer.json
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3
tokenizer.json
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version https://git-lfs.github.com/spec/v1
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||||
oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
|
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size 11421892
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30
tokenizer_config.json
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tokenizer_config.json
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{
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"add_prefix_space": false,
|
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"backend": "tokenizers",
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"bos_token": null,
|
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"clean_up_tokenization_spaces": false,
|
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"eos_token": "<|endoftext|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"is_local": false,
|
||||
"local_files_only": false,
|
||||
"model_max_length": 32768,
|
||||
"pad_token": "<|endoftext|>",
|
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"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
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