---

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 — this family (on-device, no GPU)

> Darwin Family · Aether Foundation · VKAE Accelerated · Metacognition Adapters

!POCKET-26B

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 →  

  ![Compat]() 

Pick your build →    

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

# 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 (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 (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?
• What model quantization is, and why a 4-bit model stays smart: What is model quantization?

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 — flagship, PC / server, no GPU
• 📦 POCKET-26B-GGUF — compact 26B
• 🇰🇷 POCKET-KR-GGUF — Korean, Android
• 🍎 POCKET-KR-MLX — Korean, iPhone / Mac
• 🌍 POCKET-EN-GGUF — English, phone / PC

Demos & tools (Spaces)

• 🖼️ POCKET-Image — character-perfect Korean text in images
• 🖥️ POCKET-35B-CPU — 35B answering on a CPU
• 🖥️ POCKET-26B-CPU — 26B on a CPU

📚 Full POCKET collection

<!-- /POCKET-FAMILY -->