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base_model: DoeyLLM/OneLLM-Doey-V1-Llama-3.2-3B tags:

• llama-cpp
• gguf-my-repo

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DoeyLLM/OneLLM-Doey-V1-Llama-3.2-3B-Q4KM-GGUF

This model was converted to GGUF format from DoeyLLM/OneLLM-Doey-V1-Llama-3.2-3B using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

How to Use DoeyLLM / OneLLM-Doey-V1-Llama-3.2-3B-Instruct

This guide explains how to use the DoeyLLM model on both app (iOS) and PC platforms.

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App (iOS): Use with OneLLM

OneLLM brings versatile large language models (LLMs) to your device—Llama, Gemma, Qwen, Mistral, and more. Enjoy private, offline GPT and AI tools tailored to your needs.

With OneLLM, experience the capabilities of leading-edge language models directly on your device, all without an internet connection. Get fast, reliable, and intelligent responses, while keeping your data secure with local processing.

Quick Start for iOS

Follow these steps to integrate the DoeyLLM model using the OneLLM app:

1. Download OneLLM

Get the app from the App Store and install it on your iOS device.

1. Load the DoeyLLM Model

Use the OneLLM interface to load the DoeyLLM model directly into the app: - Navigate to the Model Library. - Search for DoeyLLM. - Select the model and tap Download to store it locally on your device.

1. Start Conversing

Once the model is loaded, you can begin interacting with it through the app's chat interface. For example: - Tap the Chat tab. - Type your question or prompt, such as: > "Explain the significance of AI in education." - Receive real-time, intelligent responses generated locally.

Key Features of OneLLM

• Versatile Models: Supports various LLMs, including Llama, Gemma, and Qwen.
• Private & Secure: All processing occurs locally on your device, ensuring data privacy.
• Offline Capability: Use the app without requiring an internet connection.
• Fast Performance: Optimized for mobile devices, delivering low-latency responses.

For more details or support, visit the OneLLM App Store page.

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo DoeyLLM/OneLLM-Doey-V1-Llama-3.2-3B-Q4KM-GGUF --hf-file onellm-doey-v1-llama-3.2-3b-q4km.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo DoeyLLM/OneLLM-Doey-V1-Llama-3.2-3B-Q4KM-GGUF --hf-file onellm-doey-v1-llama-3.2-3b-q4km.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMACURL=1 flag along with other hardware-specific flags (for ex: LLAMACUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo DoeyLLM/OneLLM-Doey-V1-Llama-3.2-3B-Q4KM-GGUF --hf-file onellm-doey-v1-llama-3.2-3b-q4km.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo DoeyLLM/OneLLM-Doey-V1-Llama-3.2-3B-Q4KM-GGUF --hf-file onellm-doey-v1-llama-3.2-3b-q4km.gguf -c 2048