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fantos/Heatmap-Leaderboard-KOREA:Add Chinese labs to heatmap updated a Space 3 days ago
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Add US/EU/global labs (OpenAI/Anthropic/Google/Meta/MS/NVIDIA/Apple/Amazon/xAI/Ai2/IBM/RedHat/EleutherAI/Perplexity/Salesforce/Snowflake/Mistral/Stability/Jina/LAION/BFL/Kyutai/TII/AI21 etc.) β now Korea+China+US+EU
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Add Chinese labs to heatmap
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#1 opened 3 days ago
by ginigen-ai
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reacted to SeaWolf-AI's post with π 7 days ago
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6350
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
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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’ 2.91k β’ 33
FINAL-Bench/POCKET-KR-MLX
Text Generation β’ 15B β’ Updated β’ 641 β’ 26
FINAL-Bench/POCKET-KR-GGUF
Text Generation β’ 23B β’ Updated β’ 1.7k β’ 26
FINAL-Bench/POCKET-35B-GGUF
Text Generation β’ 35B β’ Updated β’ 21.5k β’ 61
upvoted a collection 10 days ago
reacted to SeaWolf-AI's post with π 10 days ago
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4023
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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