README.md

metadata

license: mit
base_model: google/gemma-2-270m
tags:
  - conversational-ai
  - mental-health
  - productivity
  - smartphone
  - mobile-ai
  - therapy
  - assistant
  - gemma
library_name: transformers
pipeline_tag: text-generation
model-index:
  - name: zail-ai/auramind-270m
    results:
      - task:
          type: text-generation
          name: Conversational AI
        dataset:
          type: zail-ai/auramind
          name: AuraMind Dataset
        metrics:
          - type: inference_speed
            value: 100-300ms on modern smartphones
            name: Inference Speed
          - type: memory_usage
            value: ~680MB RAM
            name: Memory Usage
          - type: parameters
            value: 270M
            name: Model Parameters

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.