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TRELLIS.2-Text-to-3D (Rerun)
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Text-to-Image โ 3D or Image-to-3D
Proto_AGI PRO
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liked a Space about 6 hours ago
FINAL-Bench/POCKET-Image-Studio liked a model about 6 hours ago
FINAL-Bench/POCKET-Image-Zimage reacted to SeaWolf-AI's post with ๐ 1 day ago
POCKET now speaks Gemma 4 โ a 26B model that loads in every app, and runs on your PC with no GPU
We're adding a Gemma-4 sibling to POCKET: POCKET-26B, built from Google's Gemma-4-26B-A4B (Apache-2.0). Our flagship POCKET-35B is a Qwen-family MoE and needs a recent llama.cpp; POCKET-26B trades a little size for the thing people kept asking for โ it just loads, everywhere, today: Ollama, LM Studio, PocketPal, MLX, any stock llama.cpp. No fork, no bleeding-edge runtime, no CUDA, no cloud.
It's a sparse Mixture-of-Experts (25.2B total, ~4B active per token), so the work per token stays small โ a real 26B that generates on a CPU with no graphics card.
Two things make it stand out:
1) Universal compatibility. Gemma 4 is a standard, widely-supported architecture, so POCKET-26B runs on the tools you already have โ no waiting for your app to add a new model type.
2) Quality that survives compression. Measured GPQA-Diamond (198 q, greedy):
โข Full base: 67.7%
โข POCKET-26B Q4_K_M (17 GB): 67.7% โ lossless
โข POCKET-26B Q2_K (11 GB): 67.2% โ near-lossless, at 11 GB
Live, on a CPU-only box (our demo Space โ POCKET-26B vs Bonsai-27B, same machine, same stock llama.cpp): POCKET-26B โ 19 tok/s vs Bonsai โ 6 tok/s โ about 3ร faster generation, no GPU. (Honest notes: shared CPU box, sequential race; a dedicated machine is faster.)
Where it fits in the family:
โข POCKET-35B (Qwen MoE) โ bigger, top-tier, needs a recent llama.cpp.
โข POCKET-26B (Gemma 4) โ loads in any app, quality-robust when compressed. The demo runs the Q4_K_M build; Q2_K (11 GB) is the smallest footprint. For a true โค8 GB phone, the 5 GB POCKET-KR (Qwen) is still the pick.
Try it and grab it:
๐ฅ๏ธ Live demo (Gemma4-based, answering on a CPU, no GPU): https://huggingface.co/spaces/FINAL-Bench/POCKET-26B-CPU
๐ฆ POCKET-26B-GGUF (Q4_K_M 17 GB ยท Q2_K 11 GB): https://huggingface.co/FINAL-Bench/POCKET-26B-GGUF
๐ POCKET collection: https://huggingface.co/collections/FINAL-Bench/pocket-modelsOrganizations
