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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:
- problem-solve
- text-generation-inference
- code
- math
---
# **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) |