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README.md

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+---
+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 🇨🇦*