README.md

---
license: apache-2.0
base_model: Qwen/Qwen3-14B
language:
  - en
  - multilingual
tags:
  - code
  - coding
  - agentic
  - code-generation
  - code-review
  - debugging
  - instruction-tuned
  - lora
  - sft
  - perciqa
  - aurora
  - canadian-ai
  - mini
pipeline_tag: text-generation
library_name: transformers
---

# Aurora-Code-Mini-V1

> *Compact. Capable. Canadian.*

Aurora-Code-Mini-V1 is a 14.8B dense coding model built by Perciqa, a Canadian AI company. Fine-tuned from Qwen3-14B on a highly curated, proprietary dataset of agentic coding instruction pairs, Aurora-Code-Mini-V1 is designed for developers who need fast, high-quality coding assistance — without cloud dependencies, usage limits, or black boxes.

**License:** Apache 2.0  
**Hardware:** Requires ~28 GB VRAM at BF16, or ~8 GB with 4-bit quantization.  
**Made in Canada** 🇨🇦

---

## What Aurora-Code-Mini-V1 Does

Aurora-Code-Mini-V1 is tuned specifically for developers who need a model they can deploy, audit, and fully control on their own infrastructure.

- **Code Generation:** Write functions, classes, and complete programs across 40+ languages.
- **Debugging:** Identify root causes and produce clear, actionable fixes.
- **Code Review:** Flag security issues, suggest refactors, and explain tradeoffs.
- **Agentic Tasks:** Multi-step tool use, planning, and repository-level reasoning.
- **Refactoring:** Modernize legacy code, apply design patterns, and improve maintainability.
- **Test Writing:** Generate unit tests, integration tests, and comprehensive test suites.

*No black boxes. No data leaving your infrastructure. Your model, your terms.*

---

## Quickstart

### Install

```bash
pip install "transformers>=4.51.0" accelerate peft
```

### Transformers (Adapter)

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

model_name = "Qwen/Qwen3-14B"
adapter_name = "Perciqa/Aurora-Code-Mini-V1"

tokenizer = AutoTokenizer.from_pretrained(model_name)
base = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto",
)
model = PeftModel.from_pretrained(base, adapter_name)

system_prompt = (
    "You are Aurora, an AI code assistant built by Perciqa. "
    "You help developers write, review, and understand code. "
    "You provide clear, correct, and complete solutions. "
    "When you're unsure, you say so."
)

messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": "Write a Python function to merge two sorted lists."},
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=1024,
    temperature=0.7,
    do_sample=True,
)
response = tokenizer.decode(outputs[0][len(inputs.input_ids[0]):], skip_special_tokens=True)
print(response)
```

### vLLM (Recommended for Production)

```bash
pip install vllm
vllm serve Perciqa/Aurora-Code-Mini-V1 --max-model-len 32768
```

Query via the OpenAI-compatible API:

```python
from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="token-abc123")

response = client.chat.completions.create(
    model="Perciqa/Aurora-Code-Mini-V1",
    messages=[
        {"role": "system", "content": "You are Aurora, an AI code assistant built by Perciqa."},
        {"role": "user", "content": "Refactor this function to be more Pythonic."},
    ],
    max_tokens=1024,
)
print(response.choices[0].message.content)
```

### Ollama

```bash
ollama run hf.co/Perciqa/Aurora-Code-Mini-V1
```

---

## Training Approach

*(Note: Specific dataset metrics, teacher model names, and internal training configurations are kept proprietary to protect Perciqa's intellectual property.)*

Aurora-Code-Mini-V1 is fine-tuned from the Qwen3-14B base model using a rigorous, multi-stage approach:

- **Proprietary Curation:** Trained on a carefully curated, high-quality dataset of agentic coding instruction pairs spanning critical developer workflows, including generation, debugging, refactoring, and testing.
- **Parameter-Efficient Fine-Tuning:** Optimized using Low-Rank Adaptation (LoRA) to preserve the base model's robust general reasoning capabilities while specializing in high-fidelity, developer-centric tasks.
- **Quality Assurance:** Checkpoints were extensively evaluated on held-out validation sets to optimize for low loss, high token accuracy, and strong generalization without overfitting.

---

## Model Details

| Field | Value |
| :--- | :--- |
| **Architecture** | Dense Transformer (GQA) |
| **Total Parameters** | 14.8B |
| **Transformer Layers** | 40 |
| **Attention Heads** | 40 (Q) / 8 (KV) |
| **Context Length** | 131,072 tokens (native) |
| **Base Model** | Qwen3-14B |
| **License** | Apache 2.0 |
| **Hardware (BF16)** | ~28 GB VRAM |
| **Hardware (4-bit)** | ~8 GB VRAM |

---

## System Prompt

For optimal performance, we recommend using the following system prompt:

> You are Aurora, an AI code assistant built by Perciqa.  
> You help developers write, review, and understand code.  
> You provide clear, correct, and complete solutions.  
> When you're unsure, you say so.

---

## About Perciqa

Perciqa is a Canadian AI company building enterprise models and tools that organisations can deploy, audit, and fully control — on their own infrastructure, on their own terms. Founded in 2023 and based in Canada 🇨🇦.

[perciqa.com](https://perciqa.com) · [GitHub](https://github.com/perciqa)

---

## License

Aurora-Code-Mini-V1 is released under the **Apache 2.0 License**.

*Made with ♥ by Perciqa 🇨🇦*