| --- |
| license: apache-2.0 |
| language: |
| - pt |
| base_model: |
| - google/gemma-3-270m |
| metrics: |
| - accuracy: 0 |
| pipeline_tag: text-generation |
| --- |
| |
| # 🐶 DogeAI-v1.5-Coder |
|
|
| DogeAI-v1.5-Coder is a **small, experimental code-focused language model** fine-tuned from **Gemma 3 (270M parameters)**. |
|
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| 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. |
|
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| --- |
|
|
| ## 🔍 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**. |
|
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| It represents: |
| - Early experimentation with fine-tuning |
| - Exploration of low-parameter models |
| - A step in understanding data quality, formatting, and model behavior |
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|
| Small models are valuable tools for understanding how language models actually work. |
|
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| --- |
|
|
| ## 🚫 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 |
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| DogeAI-v1.5-Coder is small, imperfect, and honest. |
| Its value lies in experimentation, not performance. |
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| Sometimes, understanding the limits teaches more than chasing scale. |
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| MADE BY AXIONLAB |
