prithivMLmods
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Draco-CoderMini-3B

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 ---
 library_name: transformers
-tags: []
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 ---
 library_name: transformers
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-3B-Instruct
+pipeline_tag: text-generation
+tags:
+- code
+- text-generation-inference
+- math
+- problem-solve
 ---
+![1.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/kYe0d2nFRHFV2VT96qCoh.png)
+# **Draco-CoderMini-3B**
+> **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.
+> \[!note]
+> GGUF: [https://huggingface.co/prithivMLmods/Draco-CoderMini-3B-GGUF](https://huggingface.co/prithivMLmods/Draco-CoderMini-3B-GGUF)
+---
+## **Key Features**
+1. **Qwen2 Architecture Core**
+   Built on the robust and scalable **Qwen2** transformer backbone, offering solid performance on both single-turn and multi-step code workflows.
+2. **Code-First Training Focus**
+   Fine-tuned primarily on coding datasets across Python, JavaScript, C++, and Bash, with additional coverage of software documentation, APIs, and debugging tasks.
+3. **Multi-Step Reasoning in Code**
+   Capable of breaking down complex programming problems, explaining logic, and correcting bugs—ideal for students, engineers, and software instructors.
+4. **Structured Format Proficiency**
+   Outputs syntactically correct code blocks, JSON, YAML, and Markdown—streamlining integration into tools, notebooks, and docs.
+5. **Lightweight Yet Powerful**
+   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.
+6. **Cross-Language Coding Support**
+   Generates and interprets code in 10+ languages with emphasis on real-world application, scripting, and algorithmic problem-solving.
+---
+## **Quickstart with Transformers**
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_name = "prithivMLmods/Draco-CoderMini-3B"
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+prompt = "Write a Python function to check if a number is prime."
+messages = [
+    {"role": "system", "content": "You are a helpful coding assistant."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```
+---
+## **Intended Use**
+* Code generation, translation, and refactoring
+* Teaching and tutoring in programming concepts
+* Technical documentation generation and API auto-fill
+* Debugging assistant with error analysis and fixes
+* Lightweight deployment in IDEs, coding platforms, and offline environments
+---
+## **Limitations**
+* Smaller context length compared to larger coding models (e.g., >7B)
+* May require prompt engineering for deeply nested or obscure code patterns
+* Limited fluency in non-programming natural language dialogue
+* Not optimized for purely creative writing or storytelling tasks
+---
+## **References**
+1. [Qwen2.5 Technical Report](https://arxiv.org/pdf/2412.15115)
+2. [YaRN: Efficient Context Window Extension of Large Language Models](https://arxiv.org/pdf/2309.00071)