---
license: apache-2.0
base_model: Qwen/Qwen2.5-1.5B-instruct  # Change this to your exact Qwen base model repo
tags:
- text-generation
- fine-tuned
pipeline_tag: text-generation
library_name: transformers
---


# CodeMate-Qwen

## Model Details

### Model Description

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.

The project was created to explore parameter-efficient fine-tuning techniques and build a lightweight coding assistant capable of supporting real-world development workflows.

### Developed by

Michael Moses

### Funded by

Self-funded personal research project.

### Shared by

Michael Moses

### Model Type

Causal Language Model (LLM) for Code Generation and Software Engineering Assistance.

### Language(s)

* English
* Programming Languages:

  * Python
  * JavaScript
  * TypeScript
  * HTML
  * CSS
  * SQL
  * General programming concepts

### License

Apache 2.0 (subject to the licensing terms of the base Qwen model).

### Finetuned From

Qwen/Qwen2.5-Coder-1.5B

---

## Model Sources

### Repository

GitHub: https://github.com/micymike

### Hugging Face

https://huggingface.co/micymike

### Demo

Coming Soon

---

# Uses

## Direct Use

This model is intended for:

* Code generation
* Debugging assistance
* Programming education
* Code explanation
* Refactoring recommendations
* Developer productivity workflows
* AI-assisted software development

## Downstream Use

Potential downstream applications include:

* Coding copilots
* Educational coding assistants
* Automated code review systems
* Software engineering support tools
* Programming tutors

## Out-of-Scope Use

This model is not intended for:

* Legal advice
* Medical advice
* Financial decision-making
* Safety-critical systems
* Autonomous code deployment without human review

Generated code should always be reviewed and tested before production use.

---

# Bias, Risks, and Limitations

Like all large language models, CodeMate-Qwen may:

* Generate incorrect code
* Produce insecure implementations
* Hallucinate APIs or libraries
* Miss edge cases
* Reflect biases present in training data

Users should validate all generated outputs before deployment.

---

# Recommendations

The model performs best when:

* Prompts are clear and specific
* Sufficient context is provided
* Outputs are reviewed by a developer

The model should be considered an assistant rather than a replacement for software engineering expertise.

---

# How to Get Started

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "micymike/codemate-qwen-merged"

tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto"
)

prompt = "Write a Python function that checks if a number is prime."

inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(
    **inputs,
    max_new_tokens=256
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

---

# Training Details

## Training Data

The training dataset consisted of instruction-response pairs focused on software engineering and programming-related tasks.

Examples included:

* Bug fixing
* Code generation
* Code explanation
* Refactoring
* Programming Q&A
* Developer workflow assistance

## Training Procedure

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.

### Training Regime

* Base Model: Qwen2.5-Coder-1.5B
* Fine-Tuning Method: LoRA
* Framework: Hugging Face Transformers
* PEFT Library: PEFT
* Backend: PyTorch

---

# Evaluation

## Testing Data

Evaluation was performed using programming-related prompts covering:

* Python debugging
* Code generation
* Code explanation
* Refactoring tasks

## Metrics

Evaluation focused primarily on qualitative assessment:

* Instruction-following capability
* Code correctness
* Response quality
* Programming relevance

## Results

The model demonstrated improved performance on coding-focused tasks compared to the untuned base model and showed stronger alignment with software engineering workflows.

---

# Environmental Impact

### Hardware Type

NVIDIA GPU

### Cloud Provider

Google Colab

### Compute Region

Not specified

### Carbon Emitted

Not measured

---

# Technical Specifications

## Model Architecture

Transformer-based autoregressive language model.

### Base Architecture

Qwen2.5-Coder-1.5B

### Objective

Next-token prediction optimized for coding and software engineering tasks.

---

# Compute Infrastructure

## Hardware

Google Colab GPU Environment

## Software

* Python
* PyTorch
* Transformers
* PEFT
* Hugging Face Hub

---

# Citation

```bibtex
@misc{moses2026codemateqwen,
  author = {Michael Moses},
  title = {CodeMate-Qwen: A LoRA Fine-Tuned Coding Assistant Based on Qwen2.5-Coder-1.5B},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/micymike}
}
```

---

# Model Card Authors

Michael Moses

---

# Contact

GitHub: https://github.com/micymike

Email: [mosesmichael878@gmail.com](mailto:mosesmichael878@gmail.com)

---

# Future Work

Planned improvements include:

* Larger instruction datasets
* Quantized deployments
* Benchmark evaluation on HumanEval and MBPP
* Additional programming language support
* Interactive web demo
* Advanced code review capabilities