| | --- |
| | license: apache-2.0 |
| | language: |
| | - pt |
| | base_model: |
| | - google/gemma-3-270m |
| | --- |
| | |
| | # 🐶 DogeAI-v1.5-Coder |
| |
|
| | DogeAI-v1.5-Coder is a **small, experimental code-focused language model** fine-tuned from **Gemma 3 (270M parameters)**. |
| |
|
| | This model was created as a learning and experimentation project, focusing on **code generation and completion** with limited resources. It is **not intended to compete with large-scale coding models**, but rather to explore how far a compact model can go when domain-focused. |
| |
|
| | --- |
| |
|
| | ## 🔍 Model Details |
| |
|
| | - **Base model:** Gemma 3 – 270M |
| | - **Fine-tuning type:** Supervised fine-tuning (SFT) |
| | - **Primary domain:** Programming / code-related text |
| | - **Languages:** Mixed (depends on dataset; mainly scripting-style code) |
| | - **Parameters:** ~270 million |
| | - **Context length:** Limited (inherits base model constraints) |
| |
|
| | --- |
| |
|
| | ## 🎯 Intended Use |
| |
|
| | DogeAI-v1.5-Coder is best suited for: |
| |
|
| | - Simple code completion |
| | - Small scripting examples |
| | - Educational purposes (learning how fine-tuning works) |
| | - Research on **small language models** |
| | - Benchmarking and experimentation |
| |
|
| | It performs best when: |
| | - Prompts are short and explicit |
| | - The task is narrow and well-defined |
| | - Expectations are aligned with its size |
| |
|
| | --- |
| |
|
| | ## ⚠️ Limitations |
| |
|
| | This model has **clear and expected limitations**: |
| |
|
| | - Weak long-range reasoning |
| | - Inconsistent performance on complex programming tasks |
| | - Limited generalization outside the training distribution |
| | - Not reliable for production or critical systems |
| |
|
| | These limitations are a direct consequence of its **small scale and experimental nature**. |
| |
|
| | --- |
| |
|
| | ## 🧪 Training Notes |
| |
|
| | - The model was fine-tuned on a custom dataset focused on code-related text. |
| | - No reinforcement learning or advanced alignment techniques were used. |
| | - The goal was experimentation and learning, not optimization for benchmarks. |
| |
|
| | --- |
| |
|
| | ## 📚 Why This Model Exists |
| |
|
| | DogeAI-v1.5-Coder exists as a **learning artifact**. |
| |
|
| | It represents: |
| | - Early experimentation with fine-tuning |
| | - Exploration of low-parameter models |
| | - A step in understanding data quality, formatting, and model behavior |
| |
|
| | Small models are valuable tools for understanding how language models actually work. |
| |
|
| | --- |
| |
|
| | ## 🚫 What This Model Is NOT |
| |
|
| | - ❌ A replacement for large coding assistants |
| | - ❌ A reasoning-focused model |
| | - ❌ Production-ready |
| | - ❌ Instruction-following at a high level |
| |
|
| | --- |
| |
|
| | ## 📜 License |
| |
|
| | This model follows the same license as its base model (Gemma). |
| | Please ensure compliance with the original license when using or redistributing. |
| |
|
| | --- |
| |
|
| | ## 🙌 Acknowledgements |
| |
|
| | - Google Gemma team for the base model |
| | - The open-source ML community |
| |
|
| | --- |
| |
|
| | ## 🧠 Final Note |
| |
|
| | DogeAI-v1.5-Coder is small, imperfect, and honest. |
| | Its value lies in experimentation, not performance. |
| |
|
| | Sometimes, understanding the limits teaches more than chasing scale. |
| |
|
| | MADE BY AXIONLAB |
