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Perciqa
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Model Details
Model Provider
Perciqa
Model Tree
Qwen/Qwen3-Coder-30B-A3B-Instruct
Input Modalities
Output Modalities
Supported Functionality
bash
pip install transformers>=4.51.0python
from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "perciqa/Perciqa-Aurora-Code" 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." prompt = "Write a Python function to merge two sorted lists."messages = [ {"role": "system", "content": system_prompt}, {"role": "user", "content": prompt}]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)print(tokenizer.decode(outputs[0][len(inputs.input_ids[0]):], skip_special_tokens=True))bash
vllm serve perciqa/Perciqa-Aurora-CodeThen call via OpenAI-compatible API:
python
from openai import OpenAI client = OpenAI(base_url="http://localhost:8000/v1", api_key="not-needed")response = client.chat.completions.create( model="perciqa/Perciqa-Aurora-Code", messages=[ {"role": "system", "content": "You are Aurora, an AI code assistant built by Perciqa."}, {"role": "user", "content": "Write a binary search in Rust."} ])print(response.choices[0].message.content)Monitor and trace every Aurora call with the Argus reliability engine:
python
import argusfrom openai import OpenAI argus.init(server_url="http://localhost:8000", agent_name="aurora-coder")client = OpenAI(base_url="http://localhost:11434/v1", api_key="ollama") @argus.trace(kind="agent")def aurora_code(query: str) -> str: response = client.chat.completions.create( model="perciqa/Perciqa-Aurora-Code", messages=[{"role": "user", "content": query}] ) return response.choices[0].message.content aurora_code("Refactor this function to use async/await...")| Benchmark | Score |
|---|---|
| LiveCodeBench v6 | 43.4% |
| SWE-bench Verified | 42.2% |
| HumanEval+ | 87.2% |
| MBPP+ | 80.6% |
| MultiPL-E (Python) | 72.6% |
| Field | Value |
|---|---|
| Architecture | Mixture-of-Experts (MoE) |
| Total Parameters | 30.5B |
| Activated Parameters | 3.3B |
| Layers | 48 |
| Attention Heads | 32 (Q) / 4 (KV), GQA |
| Number of Experts | 128 |
| Activated Experts | 8 |
| Context Length | 262,144 natively (up to 1M with YaRN) |
| License | Apache 2.0 |
Perciqa-Aurora-Code v1 is based on Qwen3-Coder-30B-A3B-Instruct by the Qwen Team (Alibaba Cloud).
This v1 release is a transparent rebrand -- the model weights are identical to the base model.
We've added:
@argus.trace)For the base model details: Qwen/Qwen3-Coder-30B-A3B-Instruct
| Version | What | Timeline |
|---|---|---|
| v1 | Transparent rebrand + Argus integration | Current |
| v2 | QLoRA fine-tune on synthetic coding data | Q3 2026 |
| v3 | Full fine-tune with expanded dataset | Q4 2026 |
Apache 2.0
bibtex
@misc{qwen3technicalreport, title={Qwen3 Technical Report}, author={Qwen Team}, year={2025}, eprint={2505.09388}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2505.09388},}from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "perciqa/Perciqa-Aurora-Code" 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." prompt = "Write a Python function to merge two sorted lists."messages = [ {"role": "system", "content": system_prompt}, {"role": "user", "content": prompt}]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)print(tokenizer.decode(outputs[0][len(inputs.input_ids[0]):], skip_special_tokens=True))from openai import OpenAI client = OpenAI(base_url="http://localhost:8000/v1", api_key="not-needed")response = client.chat.completions.create( model="perciqa/Perciqa-Aurora-Code", messages=[ {"role": "system", "content": "You are Aurora, an AI code assistant built by Perciqa."}, {"role": "user", "content": "Write a binary search in Rust."} ])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:11434/v1", api_key="ollama") @argus.trace(kind="agent")def aurora_code(query: str) -> str: response = client.chat.completions.create( model="perciqa/Perciqa-Aurora-Code", messages=[{"role": "user", "content": query}] ) return response.choices[0].message.content aurora_code("Refactor this function to use async/await...")@misc{qwen3technicalreport, title={Qwen3 Technical Report}, author={Qwen Team}, year={2025}, eprint={2505.09388}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2505.09388},}