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Aurora-Code-1 is tuned specifically for developers who need a model they can deploy, audit, and fully control — on their own infrastructure.
No black boxes. No data leaving your infrastructure. Your model, your terms.
bash
pip install "transformers>=4.51.0" accelerateHardware: ~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.
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)bash
pip install vllmvllm serve Perciqa/Aurora-Code-1 --max-model-len 32768Query 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)bash
ollama run hf.co/Perciqa/Aurora-Code-1Argus is Perciqa's open-source agent observability SDK — trajectory tracing, token usage, eval-in-production. Aurora-Code-1 integrates natively.
python
import argusfrom 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.
Benchmarks in progress. Independent evaluations on LiveCodeBench, SWE-bench Verified, HumanEval+, and MBPP+ will be published here before the stable release.
| 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 |
Aurora-Code-1 v1 is trained with LoRA supervised fine-tuning on a curated set of 2,700 agentic coding instruction pairs covering:
Future versions will expand the dataset and move to full fine-tuning.
| 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 |
text
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.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 🇨🇦.
Aurora-Code-1 is released under the Apache 2.0 License.
Made with ♥ by Perciqa 🇨🇦
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)pip install vllmvllm serve Perciqa/Aurora-Code-1 --max-model-len 32768from 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)import argusfrom 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)