Text Generation
PEFT
Safetensors
English
code
coding
agentic
gemma-4
code-generation
perciqa
aurora
mini
lora
sft
qlora
conversational
Instructions to use Perciqa/Aurora-Code-Mini-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Perciqa/Aurora-Code-Mini-1 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-4-12b") model = PeftModel.from_pretrained(base_model, "Perciqa/Aurora-Code-Mini-1") - Notebooks
- Google Colab
- Kaggle
Aurora-Code-Mini-1
Compact. Capable. Completely open.
Aurora-Code-Mini-1 is a 12B dense coding model built by Perciqa, a Canadian AI company. Fine-tuned from Gemma 4 12B on curated agentic coding instruction pairs using QLoRA.
Training Details
| Field | Value |
|---|---|
| Base Model | google/gemma-4-12b |
| Method | QLoRA (4-bit NF4) |
| LoRA Rank | 16 |
| LoRA Alpha | 32 |
| LoRA Dropout | 0.0 |
| Target Modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
| Precision | BF16 compute, NF4 storage |
| Context Length | 2048 tokens |
| Epochs | 3 |
| Batch Size | 2 (effective 16 with grad accum 8) |
| Learning Rate | 2e-4 |
| Warmup Steps | 10 |
| Optimizer | AdamW |
| Gradient Checkpointing | True |
Training Data
- Source: perciqa/aurora-code-sft-v1
- Train: 1,992 records
- Validation: 225 records
- Categories: Code generation, debugging, code review, agentic coding, refactoring, test writing
Hardware
- GPU: AMD Radeon W7900 (48 GB VRAM)
- Framework: ROCm 7.2.4, PyTorch 2.10.0, Transformers 5.14.1, TRL 0.24.0, PEFT 0.19.1
- Training time: ~10 hours
Quickstart
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Perciqa/Aurora-Code-Mini-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=1024,
temperature=0.7,
do_sample=True,
)
response = tokenizer.decode(outputs[0][len(inputs.input_ids[0]):], skip_special_tokens=True)
print(response)
License
Apache 2.0
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.
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