Instructions to use Perciqa/Aurora-Code-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Perciqa/Aurora-Code-1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Perciqa/Aurora-Code-1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Perciqa/Aurora-Code-1")
model = AutoModelForCausalLM.from_pretrained("Perciqa/Aurora-Code-1", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Perciqa/Aurora-Code-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Perciqa/Aurora-Code-1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Perciqa/Aurora-Code-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Perciqa/Aurora-Code-1

SGLang

How to use Perciqa/Aurora-Code-1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Perciqa/Aurora-Code-1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Perciqa/Aurora-Code-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Perciqa/Aurora-Code-1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Perciqa/Aurora-Code-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Perciqa/Aurora-Code-1 with Docker Model Runner:
```
docker model run hf.co/Perciqa/Aurora-Code-1
```

Aurora-Code-1 / README.md

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	---
	license: apache-2.0
	base_model: Qwen/Qwen3.6-35B-A3B
	language:
	- en
	- fr
	- multilingual
	tags:
	- code
	- coding
	- agentic
	- moe
	- code-generation
	- code-review
	- debugging
	- instruction-tuned
	- perciqa
	- aurora
	- canadian-ai
	- enterprise
	- lora
	- sft
	datasets:
	- perciqa/aurora-code-sft-v1
	pipeline_tag: text-generation
	library_name: transformers
	---

	# Aurora-Code-1

	> Northern Lights for your codebase.

	Aurora-Code-1 is a 35B Mixture-of-Experts coding model (3B parameters activated per token) built by [Perciqa](https://perciqa.com), a Canadian AI company. Fine-tuned from Qwen3.6-35B-A3B on 2,700 high-quality agentic coding instruction pairs, Aurora-Code-1 is optimised for code generation, debugging, code review, and multi-step agentic coding workflows.

	[![License: Apache 2.0](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
	[![HuggingFace](https://img.shields.io/badge/%F0%9F%A4%97-Perciqa%2FAurora--Code--1-yellow)](https://huggingface.co/Perciqa/Aurora-Code-1)
	[![Canadian AI](https://img.shields.io/badge/Made%20in-Canada%20%F0%9F%87%A8%F0%9F%87%A6-red)](https://perciqa.com)

	---

	## What Aurora-Code-1 does

	Aurora-Code-1 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, explain tradeoffs
	- Agentic tasks — multi-step tool use, planning, and repository-level reasoning

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

	---

	## Quickstart

	### Install

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

	> Hardware: ~60–65 GB VRAM. A single 80 GB A100/H100 or two 48 GB GPUs work well. `device_map="auto"` supports CPU offload for smaller setups.

	### Transformers

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_name = "Perciqa/Aurora-Code-1"

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

	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=2048,
	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-1 --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-1",
	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=2048,
	)
	print(response.choices[0].message.content)
	```

	### Ollama

	```bash
	ollama run hf.co/Perciqa/Aurora-Code-1
	```

	---

	## Argus Integration

	[Argus](https://perciqa.com/argus) is Perciqa's open-source agent observability SDK — trajectory tracing, token usage, eval-in-production. Aurora-Code-1 integrates natively.

	```python
	import argus
	from openai import OpenAI

	argus.init(server_url="http://localhost:8000", agent_name="aurora-coder")
	client = OpenAI(base_url="http://localhost:8000/v1", api_key="token-abc123")

	@argus.trace(kind="agent")
	def aurora_code(query: str) -> str:
	response = client.chat.completions.create(
	model="Perciqa/Aurora-Code-1",
	messages=[
	{"role": "system", "content": "You are Aurora, an AI code assistant built by Perciqa."},
	{"role": "user", "content": query},
	],
	)
	return response.choices[0].message.content

	result = aurora_code("Write a TypeScript function that validates an email address.")
	print(result)
	```

	Argus captures latency, token usage, inputs/outputs, and agent spans — visible in the Argus dashboard with no extra instrumentation.

	---

	## Performance

	Benchmarks in progress. Independent evaluations on LiveCodeBench, SWE-bench Verified, HumanEval+, and MBPP+ will be published here before the stable release.

	---

	## Model Details

	\| Field \| Value \|
	\|---\|---\|
	\| Architecture \| Mixture-of-Experts (MoE) Transformer \|
	\| Total Parameters \| 35B \|
	\| Activated Parameters \| 3B per token \|
	\| Transformer Layers \| 48 \|
	\| Attention Heads \| 32 (Q) / 4 (KV), Grouped Query Attention \|
	\| Total Experts \| 128 \|
	\| Activated Experts \| 8 per token \|
	\| Fine-Tuning \| LoRA SFT — 2,700 agentic coding pairs \|
	\| Context Length \| 131,072 tokens \|
	\| License \| Apache 2.0 \|

	---

	## Training

	Aurora-Code-1 v1 is trained with LoRA supervised fine-tuning on a curated set of 2,700 agentic coding instruction pairs covering:

	- Code generation (Python, TypeScript, Go, Rust, and more)
	- Debugging and root cause analysis
	- Code review and refactoring
	- Multi-step agentic reasoning and tool use

	Future versions will expand the dataset and move to full fine-tuning.

	---

	## Roadmap

	\| Version \| Description \| Status \|
	\|---\|---\|---\|
	\| v1 \| LoRA SFT on 2,700 agentic coding pairs. Perciqa system prompt and Argus integration. \| Released \|
	\| v2 \| Expanded SFT on 10K–20K pairs. Independent benchmark evaluation. \| Q3 2026 \|
	\| v3 \| Full fine-tune with extended dataset across generation, debugging, review, and test writing. \| Q4 2026 \|

	---

	## 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-1 is released under the [Apache 2.0 License](https://opensource.org/licenses/Apache-2.0).

	---

	Made with ♥ by [Perciqa](https://perciqa.com) 🇨🇦