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Growth Rate:
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Sponsored by
AdsCreator.com
FINAL-Bench / POCKET-26B-GGUF
Model's Last Updated: Septembre 25 2026
text-generation
Introduction of POCKET-26B-GGUF
Model Details of POCKET-26B-GGUF
📚 Collections
▶ POCKET Models — this family (on-device, no GPU) Darwin Family · Aether Foundation · VKAE Accelerated · Metacognition Adapters
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 →
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_Kholds 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.
Runs of FINAL-Bench POCKET-26B-GGUF on huggingface.co
271.4K
Total runs
0
24-hour runs
0
3-day runs
-33.3K
7-day runs
-61.7K
30-day runs
More Information About POCKET-26B-GGUF huggingface.co Model
Url of POCKET-26B-GGUF
POCKET-26B-GGUF huggingface.co Url
Provider of POCKET-26B-GGUF huggingface.co
Other API from FINAL-Bench
Updated:Septembre 25 2026
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