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--- |
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library_name: transformers |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- Qwen/Qwen2.5-3B-Instruct |
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pipeline_tag: text-generation |
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tags: |
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- problem-solve |
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- text-generation-inference |
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- code |
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- math |
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--- |
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# **Draco-CoderMini-3B** |
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> **Draco-CoderMini-3B** is a compact, coding-optimized language model built on the **Qwen2 architecture**, tailored for high-accuracy **code generation**, **debugging**, and **technical reasoning**. With **3 billion parameters**, it strikes a balance between power and deployability, making it an ideal assistant for developers, educators, and engineers working in constrained environments or requiring fast inference. |
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> \[!note] |
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> GGUF: [https://huggingface.co/prithivMLmods/Draco-CoderMini-3B-GGUF](https://huggingface.co/prithivMLmods/Draco-CoderMini-3B-GGUF) |
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--- |
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## **Key Features** |
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1. **Qwen2 Architecture Core** |
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Built on the robust and scalable **Qwen2** transformer backbone, offering solid performance on both single-turn and multi-step code workflows. |
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2. **Code-First Training Focus** |
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Fine-tuned primarily on coding datasets across Python, JavaScript, C++, and Bash, with additional coverage of software documentation, APIs, and debugging tasks. |
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3. **Multi-Step Reasoning in Code** |
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Capable of breaking down complex programming problems, explaining logic, and correcting bugs—ideal for students, engineers, and software instructors. |
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4. **Structured Format Proficiency** |
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Outputs syntactically correct code blocks, JSON, YAML, and Markdown—streamlining integration into tools, notebooks, and docs. |
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5. **Lightweight Yet Powerful** |
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At 3B parameters, it provides strong results without the heavy resource demands of larger models, and is deployable on most modern GPUs or powerful CPUs. |
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6. **Cross-Language Coding Support** |
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Generates and interprets code in 10+ languages with emphasis on real-world application, scripting, and algorithmic problem-solving. |
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--- |
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## **Quickstart with Transformers** |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "prithivMLmods/Draco-CoderMini-3B" |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype="auto", |
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device_map="auto" |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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prompt = "Write a Python function to check if a number is prime." |
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messages = [ |
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{"role": "system", "content": "You are a helpful coding assistant."}, |
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{"role": "user", "content": prompt} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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generated_ids = model.generate( |
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**model_inputs, |
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max_new_tokens=512 |
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) |
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generated_ids = [ |
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
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] |
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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print(response) |
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``` |
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--- |
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## **Intended Use** |
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* Code generation, translation, and refactoring |
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* Teaching and tutoring in programming concepts |
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* Technical documentation generation and API auto-fill |
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* Debugging assistant with error analysis and fixes |
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* Lightweight deployment in IDEs, coding platforms, and offline environments |
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--- |
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## **Limitations** |
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* Smaller context length compared to larger coding models (e.g., >7B) |
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* May require prompt engineering for deeply nested or obscure code patterns |
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* Limited fluency in non-programming natural language dialogue |
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* Not optimized for purely creative writing or storytelling tasks |
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--- |
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## **References** |
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1. [Qwen2.5 Technical Report](https://arxiv.org/pdf/2412.15115) |
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2. [YaRN: Efficient Context Window Extension of Large Language Models](https://arxiv.org/pdf/2309.00071) |
