Auramind-270M - 270M Parameters
Full-featured smartphone deployment with balanced performance and capabilities
Specifications
- Parameters: 270M
- Base Model: google/gemma-2-270m
- Memory Usage: ~680MB RAM
- Quantization: INT4 optimized
- Inference Speed: 100-300ms on modern smartphones
Mobile Deployment
This variant is specifically optimized for:
- Target Devices: Premium smartphones
- Memory Requirements: ~680MB RAM
- Performance: 100-300ms on modern smartphones
Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
# Load this specific variant
tokenizer = AutoTokenizer.from_pretrained("zail-ai/auramind-270m")
model = AutoModelForCausalLM.from_pretrained(
"zail-ai/auramind-270m",
torch_dtype=torch.float16,
device_map="auto",
low_cpu_mem_usage=True
)
Refer to the main AuraMind repository for complete documentation.
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Evaluation results
- Inference Speed on AuraMind Datasetself-reported100-300ms on modern smartphones
- Memory Usage on AuraMind Datasetself-reported~680MB RAM
- Model Parameters on AuraMind Datasetself-reported270M