PocketLLM Model Collection
The official model collection for PocketLLM Private • Offline • On-Device AI for Android
What is PocketLLM?
PocketLLM is an Android app that runs large language models completely on your device — no internet required during inference, no data sent to the cloud, no subscriptions.
This repository hosts the curated model collection optimized for mobile edge inference using:
MediaPipe LLM Inference (for .bin and .task files — Gemma family)llama.cpp via llama.rn (for .gguf files — Llama, Phi, Qwen, SmolLM, Gemma 3)
Model Catalog
⚡ Ultra Fast — Under 1 GB, fits any Android phone
Model File Size Format RAM Qwen 2.5 0.5B qwen2.5-0.5b-instruct-q4_k_m.gguf0.4 GB GGUF 2 GB Gemma 3 1B gemma-3-1b-it-q4_k_m.gguf0.7 GB GGUF 2 GB Llama 3.2 1B llama-3.2-1b-instruct-q4_k_m.gguf0.8 GB GGUF 3 GB SmolLM2 1.7B smollm2-1.7b-instruct-q4_k_m.gguf1.0 GB GGUF 3 GB
⚖️ Balanced — 1–2 GB, great quality on mid-range phones
Model File Size Format RAM Gemma 1.1 2B (CPU) gemma-1.1-2b-it-cpu-int4.bin1.35 GB MediaPipe 4 GB Gemma 1.1 2B (GPU) gemma-1.1-2b-it-gpu-int4.bin1.35 GB MediaPipe 4 GB Llama 3.2 3B llama-3.2-3b-instruct-q4_k_m.gguf2.0 GB GGUF 4 GB
🚀 Powerful — Best quality, needs 5GB+ RAM
Model File Size Format RAM Phi-3.5 Mini phi-3.5-mini-instruct-q4_k_m.gguf2.4 GB GGUF 5 GB Gemma 3 4B gemma-3-4b-it-q4_k_m.gguf2.8 GB GGUF 6 GB
How to Use in PocketLLM
Models are downloaded directly inside the PocketLLM app. Open the Model Store tab, select a model, and tap Download. The app handles everything automatically.
Direct download URLs:
https://huggingface.co/AmareshHebbar/pocketllm-models/resolve/main/<filename>Fine-Tuning Roadmap
Coming soon — within the next 2 weeks
We are fine-tuning these base models specifically for on-device conversational AI on mobile:
Goals
Better persona adherence (the model stays in character consistently)
Shorter, more natural responses (base models tend to be verbose on mobile)
Improved memory utilization (uses injected memories naturally)
Better instruction following for small context windows (2048 tokens)
Hindi + English code-switching support (for India market)
Training approach
Base: Llama 3.2 1B, Gemma 3 1B (smallest models first — fastest iteration)Method: QLoRA fine-tuning (4-bit, similar to the AxioMapper training setup)Dataset: Curated conversational dataset optimized for mobile edge constraintsHardware: Single A100 via RunPodFramework: Unsloth (2x faster fine-tuning, same as AxioMapper)
Fine-tuned models (coming soon)
pocketllm-llama-1b-v1.gguf — Llama 3.2 1B fine-tuned for mobile chatpocketllm-gemma-1b-v1.gguf — Gemma 3 1B fine-tuned for persona consistency
Model Selection Guide
Your phone has... Best model to start with
─────────────────────────────────────────────────────
2 GB RAM (budget) → Qwen 2.5 0.5B (fastest)
3 GB RAM → Llama 3.2 1B (balanced speed)
4 GB RAM (mid-range) → Gemma 1.1 2B (best all-rounder)
6 GB RAM → Llama 3.2 3B (better quality)
8 GB RAM (flagship) → Phi-3.5 Mini (best for coding)Technical Details
MediaPipe format (.bin, .task)
Used for Gemma family
Runs via Google's MediaPipe LLM Inference SDK
Supports CPU and GPU acceleration (Vulkan/OpenCL)
Integrated via react-native-llm-mediapipe
GGUF format (.gguf)
Used for Llama, Qwen, Phi, SmolLM, Gemma 3
Runs via llama.cpp (the gold standard for mobile inference)
Q4_K_M quantization — best balance of size and quality
Integrated via llama.rn
Repository Structure
pocketllm-models/
├── gemma-1.1-2b-it-cpu-int4.bin ← Gemma 2B CPU (MediaPipe)
├── gemma-1.1-2b-it-gpu-int4.bin ← Gemma 2B GPU (MediaPipe)
├── gemma-3-1b-it-q4_k_m.gguf ← Gemma 3 1B (GGUF)
├── gemma-3-4b-it-q4_k_m.gguf ← Gemma 3 4B (GGUF)
├── llama-3.2-1b-instruct-q4_k_m.gguf ← Llama 3.2 1B (GGUF)
├── llama-3.2-3b-instruct-q4_k_m.gguf ← Llama 3.2 3B (GGUF)
├── phi-3.5-mini-instruct-q4_k_m.gguf ← Phi-3.5 Mini (GGUF)
├── qwen2.5-0.5b-instruct-q4_k_m.gguf ← Qwen 2.5 0.5B (GGUF)
└── smollm2-1.7b-instruct-q4_k_m.gguf ← SmolLM2 1.7B (GGUF)License
Gemma models: Gemma Terms of Use Llama models: Meta Llama 3.2 Community License Phi models: MIT License Qwen models: Apache 2.0 SmolLM2: Apache 2.0 Fine-tuned models by PocketLLM: Apache 2.0
Built By
Amaresh Hebbar
Building PocketLLM: the only mobile app that runs a full AI agent stack — smart routing, persona memory, MCP tools — completely offline on Android.
"Your AI. Your phone. Nobody else's business."
