Instructions to use FINAL-Bench/POCKET-26B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FINAL-Bench/POCKET-26B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="FINAL-Bench/POCKET-26B-GGUF",
	filename="POCKET-26B-Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use FINAL-Bench/POCKET-26B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf FINAL-Bench/POCKET-26B-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf FINAL-Bench/POCKET-26B-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf FINAL-Bench/POCKET-26B-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf FINAL-Bench/POCKET-26B-GGUF:Q2_K

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf FINAL-Bench/POCKET-26B-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf FINAL-Bench/POCKET-26B-GGUF:Q2_K

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf FINAL-Bench/POCKET-26B-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf FINAL-Bench/POCKET-26B-GGUF:Q2_K

Use Docker

docker model run hf.co/FINAL-Bench/POCKET-26B-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use FINAL-Bench/POCKET-26B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FINAL-Bench/POCKET-26B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FINAL-Bench/POCKET-26B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FINAL-Bench/POCKET-26B-GGUF:Q2_K

Ollama
How to use FINAL-Bench/POCKET-26B-GGUF with Ollama:
```
ollama run hf.co/FINAL-Bench/POCKET-26B-GGUF:Q2_K
```

Unsloth Studio

How to use FINAL-Bench/POCKET-26B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for FINAL-Bench/POCKET-26B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for FINAL-Bench/POCKET-26B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for FINAL-Bench/POCKET-26B-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use FINAL-Bench/POCKET-26B-GGUF with Docker Model Runner:
```
docker model run hf.co/FINAL-Bench/POCKET-26B-GGUF:Q2_K
```

Lemonade

How to use FINAL-Bench/POCKET-26B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull FINAL-Bench/POCKET-26B-GGUF:Q2_K

Run and chat with the model

lemonade run user.POCKET-26B-GGUF-Q2_K

List all available models

lemonade list

POCKET-26B-GGUF / README.md

SeaWolf-AI

Remove Metacognition Adapters collection link (patent hold)

577cb62 verified about 22 hours ago

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	---
	license: apache-2.0
	library_name: llama.cpp
	pipeline_tag: text-generation
	base_model:
	- google/gemma-4-26B-A4B-it
	tags:
	- gguf
	- llama.cpp
	- conversational
	- on-device
	- mobile
	- korean
	- korean-llm
	- cpu
	- local-llm
	- edge
	- gemma
	- gemma4
	- mixture-of-experts
	- moe
	- pocket
	- vidraft
	---

	> ### 📚 Collections
	> ▶ [POCKET Models](https://huggingface.co/collections/FINAL-Bench/pocket-models-6a618ee5d23eafb7e185a5c6) — this family (on-device, no GPU)
	> [Darwin Family](https://huggingface.co/collections/FINAL-Bench/darwin-family-699987b1f652864af0122193) · [Aether Foundation](https://huggingface.co/collections/FINAL-Bench/aether-foundation-model-6a5c7f2fa1a4165c0414e53a) · [VKAE Accelerated](https://huggingface.co/collections/FINAL-Bench/vkae-accelerated-6a47231d7e7999dd8227675a)

	![POCKET-26B](./pocket26_hero.svg)

	# POCKET-26B-GGUF · 한국어

	### A Gemma4-26B-A4B-based pocket model that loads in any app today — Ollama, LM Studio, PocketPal — with no bleeding-edge runtime needed. Korean-tuned, GPU-optional.

	> 🚀 Try it live on a CPU (no GPU), no install → [![POCKET-26B demo](https://img.shields.io/badge/🤗_Space-POCKET--26B_CPU_chat-0f9d6e)](https://huggingface.co/spaces/FINAL-Bench/POCKET-26B-CPU) [![POCKET-35B demo](https://img.shields.io/badge/🤗_Space-POCKET--35B_CPU_chat-ffce3a)](https://huggingface.co/spaces/FINAL-Bench/POCKET-35B-CPU)

	[![License](https://img.shields.io/badge/License-Apache_2.0-0f6e56)](https://www.apache.org/licenses/LICENSE-2.0) [![Runtime](https://img.shields.io/badge/runtime-any_llama.cpp-1baf7a)](https://github.com/ggml-org/llama.cpp) [![Compat](https://img.shields.io/badge/loads_in-Ollama_·_LM_Studio_·_PocketPal-2a9d8f)]() [![Base](https://img.shields.io/badge/base-Gemma--4--26B--A4B-185fa5)](https://huggingface.co/google/gemma-4-26B-A4B-it)

	Pick your build → [![35B](https://img.shields.io/badge/POCKET--35B-GGUF-243456)](https://huggingface.co/FINAL-Bench/POCKET-35B-GGUF) [![KR GGUF](https://img.shields.io/badge/POCKET--KR-GGUF-7A1F3D)](https://huggingface.co/FINAL-Bench/POCKET-KR-GGUF) [![KR MLX](https://img.shields.io/badge/POCKET--KR-MLX-0f6e56)](https://huggingface.co/FINAL-Bench/POCKET-KR-MLX) [![EN GGUF](https://img.shields.io/badge/POCKET--EN-GGUF-185fa5)](https://huggingface.co/FINAL-Bench/POCKET-EN-GGUF)

	## Why this one?

	POCKET-26B takes Google's Gemma4-26B-A4B (25.2B total, ~4B active MoE, Apache-2.0) and re-quantizes it with our proprietary Korean-tuned quantization — unpruned, so quality holds. Unlike our Qwen-based POCKET (which needs a very recent `llama.cpp` build for its `qwen35moe` architecture), Gemma4 loads in every mainstream runtime today: Ollama, LM Studio, PocketPal, koboldcpp, and the browser.

	## Quality — GPQA-Diamond, greedy, 198 questions (our harness)

	\| Build \| GPQA-Diamond \| vs base \|
	\|---\|---\|---\|
	\| Gemma4-26B-A4B (base) \| 67.7% \| — \|
	\| POCKET-26B `Q4_K_M` \| 67.7% \| = base (lossless) \|
	\| POCKET-26B `Q2_K` (mixed) ⭐ \| 67.2% \| −0.5pp (≈ lossless) \|

	Single greedy pass, 198 items → ±~3 pp noise. Our proprietary Korean-tuned quantization is statistically lossless vs the base.

	## Files in this repo

	\| File \| Size \| Runs on \| Best for \|
	\|---\|---\|---\|---\|
	\| `POCKET-26B-Q4_K_M.gguf` \| 17 GB \| PC / high-RAM \| top quality \|
	\| `POCKET-26B-Q2_K.gguf` ⭐ \| 11 GB \| 12 GB phone / PC / browser \| universal daily driver \|

	> Our mixed-precision quantization keeps the most quality-critical weights at higher precision — that is why `Q2_K` holds 67.2% while a plain uniform Q2 collapses to ~44%.

	## Quickstart — loads anywhere

	```bash
	# stock llama.cpp — brew / winget / apt, or LM Studio / Ollama / PocketPal
	llama-cli -m POCKET-26B-Q2_K.gguf -p "대한민국의 수도는?" -ngl 0 -t 8
	```
	No fork, no bleeding-edge build — Gemma4 support has shipped in every mainstream runtime since April 2026.

	## Lineage (honest)

	Based on [google/gemma-4-26B-A4B-it](https://huggingface.co/google/gemma-4-26B-A4B-it) (Apache-2.0). We do not re-host it unchanged — we add our proprietary Korean-tuned quantization (VIDRAFT). We deliberately do not prune it: Gemma4's low-bit robustness collapses under pruning (measured), so we keep all 128 experts and win on quality + universal compatibility instead.

	## Limitations

	- For 8 GB phones (~5 GB budget), use [POCKET-KR-GGUF](https://huggingface.co/FINAL-Bench/POCKET-KR-GGUF) (5.1 GB) — Gemma4 cannot be shrunk that far without collapse.
	- On-device iPhone/Mac throughput not yet measured by us — community reports welcome.

	## Learn more

	- Why on-device LLMs matter, and how POCKET measures up: [Can you run a large LLM without a GPU?](https://vidraft.net/insights/on-device-llm-without-gpu.html)
	- What model quantization is, and why a 4-bit model stays smart: [What is model quantization?](https://vidraft.net/insights/what-is-quantization-llm.html)

	## License
	Apache-2.0 — use, modify, redistribute freely.

	---
	POCKET is a VIDRAFT model family. Runs anywhere, no GPU.

	<!-- POCKET-FAMILY -->

	---

	## 🧩 The POCKET Family — On-device AI by VIDRAFT

	Big models, small hardware. No GPU, no cloud.

	Models
	- 📦 [POCKET-35B-GGUF](https://huggingface.co/FINAL-Bench/POCKET-35B-GGUF) — flagship, PC / server, no GPU
	- 📦 [POCKET-26B-GGUF](https://huggingface.co/FINAL-Bench/POCKET-26B-GGUF) — compact 26B
	- 🇰🇷 [POCKET-KR-GGUF](https://huggingface.co/FINAL-Bench/POCKET-KR-GGUF) — Korean, Android
	- 🍎 [POCKET-KR-MLX](https://huggingface.co/FINAL-Bench/POCKET-KR-MLX) — Korean, iPhone / Mac
	- 🌍 [POCKET-EN-GGUF](https://huggingface.co/FINAL-Bench/POCKET-EN-GGUF) — English, phone / PC
	- 🖼️ [POCKET-Image-Zimage](https://huggingface.co/FINAL-Bench/POCKET-Image-Zimage) — character-perfect text in any image

	Demos & tools (Spaces)
	- 🎨 [POCKET-Image Studio](https://huggingface.co/spaces/FINAL-Bench/POCKET-Image-Studio) — text-in-image, generate in-page
	- 🖥️ [POCKET-35B-CPU](https://huggingface.co/spaces/FINAL-Bench/POCKET-35B-CPU) — 35B answering on a CPU
	- 🖥️ [POCKET-26B-CPU](https://huggingface.co/spaces/FINAL-Bench/POCKET-26B-CPU) — 26B on a CPU

	📚 [Full POCKET collection](https://huggingface.co/collections/FINAL-Bench/pocket-models-6a618ee5d23eafb7e185a5c6)

	<!-- /POCKET-FAMILY -->