This model is a highly efficient, massively quantized large language model engineered specifically to run natively on mobile devices. It operates entirely offline (airplane mode) while staying under a strict 2GB memory limit to preserve battery life and prevent OS background app termination.

Model Details

Model Description

This model was built from first principles to challenge the necessity of cloud-based AI for daily tasks. It brings true autonomy to the user by running natively on edge hardware. Using advanced mobile quantization techniques, the model footprint is drastically reduced without destroying reasoning capabilities. It is fine-tuned for sharp, concise mobile interactions and features LangChain integration to allow live tool calling (fetching internet data) only when a connection is available and required.

Developed by: Independent Edge AI Developer
Funded by: Independent / Self-funded
Shared by: Independent Edge AI Developer
Model type: Autoregressive Large Language Model (Heavily Quantized)
Language(s) (NLP): English
License: Apache 2.0
Finetuned from model [optional]: Small parameter base model (e.g., Llama-3-8B / Phi-3-Mini)

Model Sources

Repository: [Insert Hugging Face Repo Link Here]
Paper [optional]: N/A
Demo [optional]: [Insert Link to Mobile Demo/Video Here]

Uses

Direct Use

The model is designed for everyday individuals seeking an on-device, offline AI assistant. It is perfect for offline question answering, simple text summarization, brainstorming, and daily queries where privacy, independence, and zero latency are required.

Downstream Use

Developers can integrate this model into privacy-first mobile applications, offline communication tools, or edge-computing environments where internet connectivity is restricted or highly monitored.

Out-of-Scope Use

This model is heavily compressed to fit under 2GB of RAM. It is strictly not intended for highly complex mathematical reasoning, deep coding tasks, or mission-critical factual recall (without LangChain tool calling). It should not be deployed on centralized cloud servers where full-precision models are more appropriate.

Bias, Risks, and Limitations

Due to the extreme quantization required to meet the strict memory budget, the model is susceptible to hallucinations and slight degradation in complex logical reasoning compared to full-precision cloud models. Responses are optimized to be concise; therefore, nuanced or highly detailed academic explanations may lack depth.

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. For factual or live queries, users are strongly encouraged to enable the LangChain tool-calling feature (if an internet connection is available) to ground the model's responses in real-time data.

How to Get Started with the Model

Use the code below to get started with the model using Ollama and LangChain for live tool calling.

python

from langchain_community.llms import Ollama
from langchain.agents import initialize_agent, AgentType
from langchain.tools import Tool

# Initialize the optimized local mobile model
llm = Ollama(model="your-mobile-quantized-model")

# Example LangChain tool for fetching live data when connected
def fetch_live_data(query):
    # Integration for live internet search
    return "Live data result"

tools = [
    Tool(
        name="Live Search",
        func=fetch_live_data,
        description="Useful for answering questions about current events."
    )
]

agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)

# Run offline or online
response = agent.run("What is the quick summary of my notes?")
print(response)

Training Details

Training Data

The model was fine-tuned on a curated dataset of short-form, mobile-first conversational interactions to train it to deliver sharp, concise, and highly relevant answers suitable for a mobile screen.

Training Procedure

Preprocessing

Data was formatted into highly structured instruction-response pairs to maximize prompt adherence during fine-tuning.

Training Hyperparameters

Training regime: Fine-tuned locally via Ollama, followed by extreme mobile quantization (e.g., 4-bit or 2-bit GGUF/AWQ formatting) to compress the weights and hit the strict <2GB RAM target.

Speeds, Sizes, Times

Memory Footprint: < 2GB RAM
Target Device: Standard iOS/Android smartphone
Battery Impact: Minimized due to strict memory and compute throttling constraints.

Evaluation

Testing Data, Factors & Metrics

Testing Data

Evaluated against standard mobile-use conversational benchmarks and offline reasoning tasks.

Factors

Evaluations disaggregated by hardware constraints (available RAM, thermal throttling, battery drain rates).

Metrics

RAM Usage: Measured strictly to ensure it stays below the 2GB threshold to prevent OS process killing.
Inference Speed: Tokens per second (TPS) on standard mobile ARM processors.
Battery Drain: Percentage of battery consumed per 10 minutes of active inference.

Results

The model successfully runs natively on mobile hardware without exceeding 2GB RAM. It maintains a stable token generation rate without causing the phone to overheat or aggressively drain the battery.

Summary

A highly capable edge-optimized model that balances the physical limitations of a smartphone with the utility of a daily AI assistant.

Environmental Impact

Because this model runs locally on a mobile device, it entirely bypasses the massive energy costs and cooling requirements of centralized data centers.

Hardware Type: Mobile ARM Processors / NPU
Hours used: Variable (On-device)
Cloud Provider: None (100% Local)
Compute Region: Local/Edge
Carbon Emitted: Near zero (Powered by standard smartphone battery charging)

Technical Specifications

Model Architecture and Objective

Transformer-based Large Language Model, heavily quantized and compressed for mobile edge inference. The objective is to maximize daily assistant utility while minimizing hardware taxation.

Compute Infrastructure

Designed entirely for edge compute environments.

Hardware

Mobile Phones (iOS and Android devices).

Software

Ollama (for local fine-tuning and inference management)
LangChain (for live internet tool calling when online)

Citation

BibTeX:

bibtex

@misc{mobile_quantized_llm_2024,
  title={Mobile-Optimized Local LLM (Under 2GB RAM)},
  author={Independent Edge AI Developer},
  year={2024},
  howpublished={\url{https://huggingface.co/your-repo-link}}
}

APA:

Independent Edge AI Developer. (2024). Mobile-Optimized Local LLM (Under 2GB RAM). Hugging Face. https://huggingface.co/your-repo-link

Glossary

Quantization: A technique to reduce the precision of the numbers used to represent a model's parameters, drastically shrinking the file size and memory required to run it.
Edge Computing: Running data processing (like AI inference) locally on the device (the "edge" of the network) rather than relying on a central cloud server.
Ollama: A framework used to easily set up and run large language models locally.
LangChain: A framework for developing applications powered by language models, particularly useful for giving the model "tools" to access live data.

More Information

For further details on how the quantization was performed or to contribute to the project, please visit the repository link.

Model Card Authors

Independent Edge AI Developer

Model Card Contact

`[Insert Contact Information or Repository Issue Tracker Link]

Meet0006/Llama-3-Support-Pro-V2

Mobile-Optimized Local LLM (Under 2GB RAM)