FINAL-Bench/POCKET-Image-Zimage
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πΌοΈ POCKET-Image β the POCKET series goes visual: character-perfect text in any language, on-device
A new model in VIDRAFT's POCKET family. POCKET put 35B-class models on phones and no-GPU PCs. POCKET-Image carries the same "big capability, small hardware" idea into image generation β and fixes the one thing nearly every image model gets wrong: text.
Type "μλ
νμΈμ" into a typical model and you get "μγ
κΈ°." Hangul alone composes 11,172 syllable blocks; Arabic connects its letters; Thai stacks marks. Diffusion models draw scripts as shapes, so they smear. POCKET-Image renders every glyph exactly β νκ΅μ΄ Β· δΈζ Β· ζ₯ζ¬θͺ Β· Ψ§ΩΨΉΨ±Ψ¨ΩΨ© (RTL) Β· ΰΉΰΈΰΈ’ Β· Latin and more β onto any scene you describe.
What it is:
β’ 100% accurate text, any language β where global models produce gibberish
β’ Any background from a prompt β text is optional (empty β a pure image)
β’ No GPU, no NPU β runs on plain CPU + RAM via the POCKET-Core engine
β’ Measured footprint: 8.6 GB (RTX 3050/4060) Β· 4.5 GB (offloaded, 6 GB cards) Β· 13.4 GB (MacBook, 16 GB+)
β’ Windows Β· macOS Β· Linux Β· fully local, no cloud
Built on the open, commercial-friendly Z-Image (Apache-2.0) foundation.
Honest note: the text is the guaranteed-correct part β the surrounding scene is ordinary generation, so a busy foreground can crowd the letters. We say so; clean backgrounds stay razor-sharp.
π¨ Studio β generate right here, any language:
https://huggingface.co/spaces/FINAL-Bench/POCKET-Image-Studio
𧩠Model card:
https://huggingface.co/FINAL-Bench/POCKET-Image-Zimage
π The POCKET collection:
https://huggingface.co/collections/FINAL-Bench/pocket-modelsOrganizations
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πΌοΈ POCKET-Image β the POCKET series goes visual: character-perfect text in any language, on-device
A new model in VIDRAFT's POCKET family. POCKET put 35B-class models on phones and no-GPU PCs. POCKET-Image carries the same "big capability, small hardware" idea into image generation β and fixes the one thing nearly every image model gets wrong: text.
Type "μλ νμΈμ" into a typical model and you get "μγ κΈ°." Hangul alone composes 11,172 syllable blocks; Arabic connects its letters; Thai stacks marks. Diffusion models draw scripts as shapes, so they smear. POCKET-Image renders every glyph exactly β νκ΅μ΄ Β· δΈζ Β· ζ₯ζ¬θͺ Β· Ψ§ΩΨΉΨ±Ψ¨ΩΨ© (RTL) Β· ΰΉΰΈΰΈ’ Β· Latin and more β onto any scene you describe.
What it is:
β’ 100% accurate text, any language β where global models produce gibberish
β’ Any background from a prompt β text is optional (empty β a pure image)
β’ No GPU, no NPU β runs on plain CPU + RAM via the POCKET-Core engine
β’ Measured footprint: 8.6 GB (RTX 3050/4060) Β· 4.5 GB (offloaded, 6 GB cards) Β· 13.4 GB (MacBook, 16 GB+)
β’ Windows Β· macOS Β· Linux Β· fully local, no cloud
Built on the open, commercial-friendly Z-Image (Apache-2.0) foundation.
Honest note: the text is the guaranteed-correct part β the surrounding scene is ordinary generation, so a busy foreground can crowd the letters. We say so; clean backgrounds stay razor-sharp.
π¨ Studio β generate right here, any language:
FINAL-Bench/POCKET-Image-Studio
𧩠Model card:
FINAL-Bench/POCKET-Image-Zimage
π The POCKET collection:
https://huggingface.co/collections/FINAL-Bench/pocket-models
A new model in VIDRAFT's POCKET family. POCKET put 35B-class models on phones and no-GPU PCs. POCKET-Image carries the same "big capability, small hardware" idea into image generation β and fixes the one thing nearly every image model gets wrong: text.
Type "μλ νμΈμ" into a typical model and you get "μγ κΈ°." Hangul alone composes 11,172 syllable blocks; Arabic connects its letters; Thai stacks marks. Diffusion models draw scripts as shapes, so they smear. POCKET-Image renders every glyph exactly β νκ΅μ΄ Β· δΈζ Β· ζ₯ζ¬θͺ Β· Ψ§ΩΨΉΨ±Ψ¨ΩΨ© (RTL) Β· ΰΉΰΈΰΈ’ Β· Latin and more β onto any scene you describe.
What it is:
β’ 100% accurate text, any language β where global models produce gibberish
β’ Any background from a prompt β text is optional (empty β a pure image)
β’ No GPU, no NPU β runs on plain CPU + RAM via the POCKET-Core engine
β’ Measured footprint: 8.6 GB (RTX 3050/4060) Β· 4.5 GB (offloaded, 6 GB cards) Β· 13.4 GB (MacBook, 16 GB+)
β’ Windows Β· macOS Β· Linux Β· fully local, no cloud
Built on the open, commercial-friendly Z-Image (Apache-2.0) foundation.
Honest note: the text is the guaranteed-correct part β the surrounding scene is ordinary generation, so a busy foreground can crowd the letters. We say so; clean backgrounds stay razor-sharp.
π¨ Studio β generate right here, any language:
FINAL-Bench/POCKET-Image-Studio
𧩠Model card:
FINAL-Bench/POCKET-Image-Zimage
π The POCKET collection:
https://huggingface.co/collections/FINAL-Bench/pocket-models
reacted to SeaWolf-AI's post with π₯ 4 days ago
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POCKET now speaks Gemma 4 β 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 β 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 β 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 β no waiting for your app to add a new model type.
2) Quality that survives compression. Measured GPQA-Diamond (198 q, greedy):
β’ Full base: 67.7%
β’ POCKET-26B Q4_K_M (17 GB): 67.7% β lossless
β’ POCKET-26B Q2_K (11 GB): 67.2% β near-lossless, at 11 GB
Live, on a CPU-only box (our demo Space β POCKET-26B vs Bonsai-27B, same machine, same stock llama.cpp): POCKET-26B β 19 tok/s vs Bonsai β 6 tok/s β about 3Γ faster generation, no GPU. (Honest notes: shared CPU box, sequential race; a dedicated machine is faster.)
Where it fits in the family:
β’ POCKET-35B (Qwen MoE) β bigger, top-tier, needs a recent llama.cpp.
β’ POCKET-26B (Gemma 4) β 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 β€8 GB phone, the 5 GB POCKET-KR (Qwen) is still the pick.
Try it and grab it:
π₯οΈ Live demo (Gemma4-based, answering on a CPU, no GPU): FINAL-Bench/POCKET-26B-CPU
π¦ POCKET-26B-GGUF (Q4_K_M 17 GB Β· Q2_K 11 GB): FINAL-Bench/POCKET-26B-GGUF
π POCKET collection: https://huggingface.co/collections/FINAL-Bench/pocket-models
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 β 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 β 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 β no waiting for your app to add a new model type.
2) Quality that survives compression. Measured GPQA-Diamond (198 q, greedy):
β’ Full base: 67.7%
β’ POCKET-26B Q4_K_M (17 GB): 67.7% β lossless
β’ POCKET-26B Q2_K (11 GB): 67.2% β near-lossless, at 11 GB
Live, on a CPU-only box (our demo Space β POCKET-26B vs Bonsai-27B, same machine, same stock llama.cpp): POCKET-26B β 19 tok/s vs Bonsai β 6 tok/s β about 3Γ faster generation, no GPU. (Honest notes: shared CPU box, sequential race; a dedicated machine is faster.)
Where it fits in the family:
β’ POCKET-35B (Qwen MoE) β bigger, top-tier, needs a recent llama.cpp.
β’ POCKET-26B (Gemma 4) β 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 β€8 GB phone, the 5 GB POCKET-KR (Qwen) is still the pick.
Try it and grab it:
π₯οΈ Live demo (Gemma4-based, answering on a CPU, no GPU): FINAL-Bench/POCKET-26B-CPU
π¦ POCKET-26B-GGUF (Q4_K_M 17 GB Β· Q2_K 11 GB): FINAL-Bench/POCKET-26B-GGUF
π POCKET collection: https://huggingface.co/collections/FINAL-Bench/pocket-models
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Article
POCKET: a 35-billion-parameter model that runs on your iPhone β and on your PC with no GPU
FINAL-Bench
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FINAL-Bench/POCKET-EN-GGUF
Text Generation β’ 23B β’ Updated β’ 1.69k β’ 31
FINAL-Bench/POCKET-KR-MLX
Text Generation β’ 15B β’ Updated β’ 525 β’ 24
FINAL-Bench/POCKET-KR-GGUF
Text Generation β’ 23B β’ Updated β’ 1.29k β’ 24
FINAL-Bench/POCKET-35B-GGUF
Text Generation β’ 35B β’ Updated β’ 8.23k β’ 55
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π± POCKET β a 35-billion-parameter model that runs on your iPhone, and on your PC with no GPU
We're releasing POCKET, VIDRAFT's flagship Darwin-36B-Opus compressed for on-device use. No fork, no CUDA, no cloud β it runs on stock llama.cpp. It's a sparse Mixture-of-Experts model (256 experts, only 8 active per token), so the file can be large while the work per token stays small. That's what lets a 35B model run on a phone, and generate fast on a CPU with no graphics card.
Measured (POCKET-35B IQ1_M vs Bonsai-27B Q1_0):
β’ CPU generate (Xeon, 16 threads): 27.0 vs 10.1 tok/s β 2.69Γ faster
β’ GPU generate (H100): 197 vs 89 tok/s β 2.22Γ faster
β’ GPU prompt processing (H100): 753 vs 1816 β 0.41Γ (Bonsai wins this one β MoE prefill wakes every expert, so sparsity stops helping there. We say so.)
β’ Quality (HellaSwag, 400 q): 61.0% vs 60.0% β a tie (confidence intervals overlap)
On a real consumer laptop β MacBook M3 Pro (18 GB) β POCKET wins every axis, prompt processing included:
β’ Metal generate: 25.4 vs 12.8 β 1.99Γ
β’ CPU generate: 13.8 vs 4.4 β 3.13Γ
β’ Metal prompt: 240.7 vs 73.4 β 3.28Γ
One more quiet fact: the same-size, quality-oriented rival Ternary-Bonsai-27B (7.2 GB) fails to load in upstream llama.cpp at all β it needs the PrismML fork. POCKET runs on the tools you already have: LM Studio, Ollama, PocketPal, MLX.
π Full story (tech, measurements, recipes): https://huggingface.co/blog/FINAL-Bench/pocket
Models:
π¦ POCKET-35B-GGUF (PC / server, no GPU): FINAL-Bench/POCKET-35B-GGUF
π°π· POCKET-KR-GGUF (Android): FINAL-Bench/POCKET-KR-GGUF
π POCKET-KR-MLX (iPhone / Mac): FINAL-Bench/POCKET-KR-MLX
π POCKET-EN-GGUF (English phone / PC): FINAL-Bench/POCKET-EN-GGUF
π₯οΈ Live demo (answering on a CPU, no GPU): FINAL-Bench/POCKET-35B-CPU
π Collection: FINAL-Bench/pocket-models-6a618ee5d23eafb7e185a5c6
We're releasing POCKET, VIDRAFT's flagship Darwin-36B-Opus compressed for on-device use. No fork, no CUDA, no cloud β it runs on stock llama.cpp. It's a sparse Mixture-of-Experts model (256 experts, only 8 active per token), so the file can be large while the work per token stays small. That's what lets a 35B model run on a phone, and generate fast on a CPU with no graphics card.
Measured (POCKET-35B IQ1_M vs Bonsai-27B Q1_0):
β’ CPU generate (Xeon, 16 threads): 27.0 vs 10.1 tok/s β 2.69Γ faster
β’ GPU generate (H100): 197 vs 89 tok/s β 2.22Γ faster
β’ GPU prompt processing (H100): 753 vs 1816 β 0.41Γ (Bonsai wins this one β MoE prefill wakes every expert, so sparsity stops helping there. We say so.)
β’ Quality (HellaSwag, 400 q): 61.0% vs 60.0% β a tie (confidence intervals overlap)
On a real consumer laptop β MacBook M3 Pro (18 GB) β POCKET wins every axis, prompt processing included:
β’ Metal generate: 25.4 vs 12.8 β 1.99Γ
β’ CPU generate: 13.8 vs 4.4 β 3.13Γ
β’ Metal prompt: 240.7 vs 73.4 β 3.28Γ
One more quiet fact: the same-size, quality-oriented rival Ternary-Bonsai-27B (7.2 GB) fails to load in upstream llama.cpp at all β it needs the PrismML fork. POCKET runs on the tools you already have: LM Studio, Ollama, PocketPal, MLX.
π Full story (tech, measurements, recipes): https://huggingface.co/blog/FINAL-Bench/pocket
Models:
π¦ POCKET-35B-GGUF (PC / server, no GPU): FINAL-Bench/POCKET-35B-GGUF
π°π· POCKET-KR-GGUF (Android): FINAL-Bench/POCKET-KR-GGUF
π POCKET-KR-MLX (iPhone / Mac): FINAL-Bench/POCKET-KR-MLX
π POCKET-EN-GGUF (English phone / PC): FINAL-Bench/POCKET-EN-GGUF
π₯οΈ Live demo (answering on a CPU, no GPU): FINAL-Bench/POCKET-35B-CPU
π Collection: FINAL-Bench/pocket-models-6a618ee5d23eafb7e185a5c6
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