nvidia/simready-assets
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liked a dataset 1 day ago
nvidia/simready-assets reacted to SeaWolf-AI's post with 🚀 1 day ago
No GPU. No cloud. Just your phone.
Most “small” models still beg for a datacenter. Ours fit in your pocket.
VIDRAFT POCKET is a family of GGUF models that run on an iPhone and on a GPU-free PC, straight out of the box with llama.cpp, Ollama, LM Studio, and PocketPal. Seven weeks after launch they crossed 1 million downloads. Today the family is past 2.81 million and still climbing, every day.
Three models, one pocket:
🟢 POCKET-35B — a 35B mixture-of-experts that runs on an iPhone and on a PC with no GPU. Balanced power.
→ https://huggingface.co/FINAL-Bench/POCKET-35B-GGUF
🔵 POCKET-26B — a Gemma-4 based 26B MoE, tuned for on-device Korean. Lightweight and fast.
→ https://huggingface.co/FINAL-Bench/POCKET-26B-GGUF
🟣 POCKET-Darwin-180B — a 180B flagship running on a laptop in 4-bit GGUF. The datacenter, on your desk.
→ https://huggingface.co/FINAL-Bench/POCKET-Darwin-180B-GGUF
The whole family lives here: https://huggingface.co/FINAL-Bench
Try our models live in the browser: https://huggingface.co/spaces/FINAL-Bench/POCKET-Image-Studio
Private by default. Offline by design. Yours to run.
#OnDeviceAI #LocalLLM #llamacpp #GGUF #VIDRAFT #POCKET reacted to tardellirs's post with 🔥 1 day ago
Model cards say what a model is: a fine-tune of X, a quantization of Y. I built Weightprint to check that against the weights.
App: https://huggingface.co/spaces/tardellirs/weightprint
Data (every pair, and the copy groups): https://huggingface.co/datasets/tardellirs/weightprint
It reads the header and a few bytes of every tensor (no downloads) and compares a model with the parent its card declares. 120,976 models so far. Some of what came out:
- 12% of declared fine-tunes hold their parent's weights unchanged.
- 23% of declared fine-tunes are LoRA-style: only the attention and MLP matrices moved, norms and embeddings untouched. That share went from 10% in 2023 to 25% in 2026.
- Of 1,668 fine-tunes named abliterated, uncensored or heretic, 53% edit only the matrices that write to the residual stream, the signature of an abliteration. The rest were trained further, merged, or are the parent's weights.
- 51% of the weight bytes on the Hub are byte-for-byte repeats of another repo's files.
Search any Hub id to see which layers and parts changed against its parent, or compare any two models.
A sample per tensor can miss things, and some formats can't be compared yet. If a verdict on a model you know looks wrong, tell me here and I'll check it against the engine. That's what this post is for, before a longer write-up.Organizations
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liked a
dataset 1 day ago
reacted to SeaWolf-AI's post with 🚀 1 day ago
Post
4,556
No GPU. No cloud. Just your phone.
Most “small” models still beg for a datacenter. Ours fit in your pocket.
VIDRAFT POCKET is a family of GGUF models that run on an iPhone and on a GPU-free PC, straight out of the box with llama.cpp, Ollama, LM Studio, and PocketPal. Seven weeks after launch they crossed 1 million downloads. Today the family is past 2.81 million and still climbing, every day.
Three models, one pocket:
🟢 POCKET-35B — a 35B mixture-of-experts that runs on an iPhone and on a PC with no GPU. Balanced power.
→ FINAL-Bench/POCKET-35B-GGUF
🔵 POCKET-26B — a Gemma-4 based 26B MoE, tuned for on-device Korean. Lightweight and fast.
→ FINAL-Bench/POCKET-26B-GGUF
🟣 POCKET-Darwin-180B — a 180B flagship running on a laptop in 4-bit GGUF. The datacenter, on your desk.
→ FINAL-Bench/POCKET-Darwin-180B-GGUF
The whole family lives here:
FINAL-Bench
Try our models live in the browser: FINAL-Bench/POCKET-Image-Studio
Private by default. Offline by design. Yours to run.
#OnDeviceAI #LocalLLM #llamacpp #GGUF #VIDRAFT #POCKET
Most “small” models still beg for a datacenter. Ours fit in your pocket.
VIDRAFT POCKET is a family of GGUF models that run on an iPhone and on a GPU-free PC, straight out of the box with llama.cpp, Ollama, LM Studio, and PocketPal. Seven weeks after launch they crossed 1 million downloads. Today the family is past 2.81 million and still climbing, every day.
Three models, one pocket:
🟢 POCKET-35B — a 35B mixture-of-experts that runs on an iPhone and on a PC with no GPU. Balanced power.
→ FINAL-Bench/POCKET-35B-GGUF
🔵 POCKET-26B — a Gemma-4 based 26B MoE, tuned for on-device Korean. Lightweight and fast.
→ FINAL-Bench/POCKET-26B-GGUF
🟣 POCKET-Darwin-180B — a 180B flagship running on a laptop in 4-bit GGUF. The datacenter, on your desk.
→ FINAL-Bench/POCKET-Darwin-180B-GGUF
The whole family lives here:
Try our models live in the browser: FINAL-Bench/POCKET-Image-Studio
Private by default. Offline by design. Yours to run.
#OnDeviceAI #LocalLLM #llamacpp #GGUF #VIDRAFT #POCKET
- 3 replies
reacted to tardellirs's post with 🔥 1 day ago
Post
2,743
Model cards say what a model is: a fine-tune of X, a quantization of Y. I built Weightprint to check that against the weights.
App: tardellirs/weightprint
Data (every pair, and the copy groups): tardellirs/weightprint
It reads the header and a few bytes of every tensor (no downloads) and compares a model with the parent its card declares. 120,976 models so far. Some of what came out:
- 12% of declared fine-tunes hold their parent's weights unchanged.
- 23% of declared fine-tunes are LoRA-style: only the attention and MLP matrices moved, norms and embeddings untouched. That share went from 10% in 2023 to 25% in 2026.
- Of 1,668 fine-tunes named abliterated, uncensored or heretic, 53% edit only the matrices that write to the residual stream, the signature of an abliteration. The rest were trained further, merged, or are the parent's weights.
- 51% of the weight bytes on the Hub are byte-for-byte repeats of another repo's files.
Search any Hub id to see which layers and parts changed against its parent, or compare any two models.
A sample per tensor can miss things, and some formats can't be compared yet. If a verdict on a model you know looks wrong, tell me here and I'll check it against the engine. That's what this post is for, before a longer write-up.
App: tardellirs/weightprint
Data (every pair, and the copy groups): tardellirs/weightprint
It reads the header and a few bytes of every tensor (no downloads) and compares a model with the parent its card declares. 120,976 models so far. Some of what came out:
- 12% of declared fine-tunes hold their parent's weights unchanged.
- 23% of declared fine-tunes are LoRA-style: only the attention and MLP matrices moved, norms and embeddings untouched. That share went from 10% in 2023 to 25% in 2026.
- Of 1,668 fine-tunes named abliterated, uncensored or heretic, 53% edit only the matrices that write to the residual stream, the signature of an abliteration. The rest were trained further, merged, or are the parent's weights.
- 51% of the weight bytes on the Hub are byte-for-byte repeats of another repo's files.
Search any Hub id to see which layers and parts changed against its parent, or compare any two models.
A sample per tensor can miss things, and some formats can't be compared yet. If a verdict on a model you know looks wrong, tell me here and I'll check it against the engine. That's what this post is for, before a longer write-up.
- 2 replies
liked 5
Spaces 1 day ago
BFS Best Face Swap
🎭
758
Head swap with the BFS LoRA on Qwen-Image-2.1 (+turbo)
SimReady Central
🦾
370
Semantic search for 3D assets: USD + RTX simulation previews
Wan2.2 14B Preview
🐌
1.3k
generate a video from an image with a text prompt
Paper Pulse
📄
26
Upvote history of every Hugging Face Daily Paper
Weightprint
🧬
3
What a fine-tune changed, measured from its weights
reacted to savoji's post with 🚀 1 day ago
Post
4,744
LFM3-1.7B from @LiquidAI is the #1 trending model on the Hub (855 likes). It takes about 1 GB at 4-bit, and in the vendor's own table it beats Qwen3.5 9B on several agent benchmarks.
The edge decision moves from "are small models good enough?" to "which architecture and runtime do I pick?" One 1.7B backbone now ships as text, VL-2B and Omni-2B checkpoints with open weights.
🔍 Architecture and speed
• 24 layers: 13 gated convolutions (kernel 7), 5 sliding-window attention (256 tokens), 6 global attention, chosen by a search over 14 device and backend targets
• llama.cpp Q4_0 at 32K context: 1.8 to 2.8x faster decode than the next model on 3 devices
• Full 128K context needs 1.6 GB of KV cache vs 4.3 GB (Qwen3.5-4B) and 5.6 GB (MiniCPM5-2B)
📊 Liquid AI's table
• Average 49.37 vs 47.24 for Qwen3.5 9B; BFCL V4 71.39 vs 62.83; WebArena-Verified 38.42 vs 28.94
• Qwen3.5 9B still wins SWE-bench Verified (51.20 vs 43.40) and GPQA Diamond (80.40 vs 63.37)
🛠️ Try it
• GGUF (174,177 downloads last month) runs in llama.cpp b11960+, Ollama and LM Studio; MLX 4-bit for Apple Silicon; SGLang for GPUs
• llama-cli -hf LiquidAI/LFM3-1.7B-GGUF:Q4_K_M --temp 0.1 --top-k 50 --repeat-penalty 1.1
• Omni-2B runs in the browser in the WebGPU Space
⚠️ Caveats
• The numbers are vendor-reported, with no independent reproduction in the sources. The card says it is not for agentic coding or complex multi-step reasoning.
LiquidAI/LFM3-1.7B
LiquidAI/LFM3-1.7B-GGUF
LiquidAI/LFM3-Omni-2B
LiquidAI/LFM3-Omni-2B-WebGPU
Has a sub-2B model held up on your own tool-calling harness?
• Medium: https://medium.com/p/05507e4e81c8
• Substack: https://sajjadpakdamansavoji.substack.com/p/liquid-ais-lfm3-turns-the-small-model
• X: https://x.com/saavoji/status/2117307514177941672
• Website: https://sajjadpsavoji.github.io/blog#liquid-ais-lfm3
The edge decision moves from "are small models good enough?" to "which architecture and runtime do I pick?" One 1.7B backbone now ships as text, VL-2B and Omni-2B checkpoints with open weights.
🔍 Architecture and speed
• 24 layers: 13 gated convolutions (kernel 7), 5 sliding-window attention (256 tokens), 6 global attention, chosen by a search over 14 device and backend targets
• llama.cpp Q4_0 at 32K context: 1.8 to 2.8x faster decode than the next model on 3 devices
• Full 128K context needs 1.6 GB of KV cache vs 4.3 GB (Qwen3.5-4B) and 5.6 GB (MiniCPM5-2B)
📊 Liquid AI's table
• Average 49.37 vs 47.24 for Qwen3.5 9B; BFCL V4 71.39 vs 62.83; WebArena-Verified 38.42 vs 28.94
• Qwen3.5 9B still wins SWE-bench Verified (51.20 vs 43.40) and GPQA Diamond (80.40 vs 63.37)
🛠️ Try it
• GGUF (174,177 downloads last month) runs in llama.cpp b11960+, Ollama and LM Studio; MLX 4-bit for Apple Silicon; SGLang for GPUs
• llama-cli -hf LiquidAI/LFM3-1.7B-GGUF:Q4_K_M --temp 0.1 --top-k 50 --repeat-penalty 1.1
• Omni-2B runs in the browser in the WebGPU Space
⚠️ Caveats
• The numbers are vendor-reported, with no independent reproduction in the sources. The card says it is not for agentic coding or complex multi-step reasoning.
LiquidAI/LFM3-1.7B
LiquidAI/LFM3-1.7B-GGUF
LiquidAI/LFM3-Omni-2B
LiquidAI/LFM3-Omni-2B-WebGPU
Has a sub-2B model held up on your own tool-calling harness?
• Medium: https://medium.com/p/05507e4e81c8
• Substack: https://sajjadpakdamansavoji.substack.com/p/liquid-ais-lfm3-turns-the-small-model
• X: https://x.com/saavoji/status/2117307514177941672
• Website: https://sajjadpsavoji.github.io/blog#liquid-ais-lfm3
- 12 replies
reacted to tardellirs's post with 🚀 1 day ago
Post
2,743
Model cards say what a model is: a fine-tune of X, a quantization of Y. I built Weightprint to check that against the weights.
App: tardellirs/weightprint
Data (every pair, and the copy groups): tardellirs/weightprint
It reads the header and a few bytes of every tensor (no downloads) and compares a model with the parent its card declares. 120,976 models so far. Some of what came out:
- 12% of declared fine-tunes hold their parent's weights unchanged.
- 23% of declared fine-tunes are LoRA-style: only the attention and MLP matrices moved, norms and embeddings untouched. That share went from 10% in 2023 to 25% in 2026.
- Of 1,668 fine-tunes named abliterated, uncensored or heretic, 53% edit only the matrices that write to the residual stream, the signature of an abliteration. The rest were trained further, merged, or are the parent's weights.
- 51% of the weight bytes on the Hub are byte-for-byte repeats of another repo's files.
Search any Hub id to see which layers and parts changed against its parent, or compare any two models.
A sample per tensor can miss things, and some formats can't be compared yet. If a verdict on a model you know looks wrong, tell me here and I'll check it against the engine. That's what this post is for, before a longer write-up.
App: tardellirs/weightprint
Data (every pair, and the copy groups): tardellirs/weightprint
It reads the header and a few bytes of every tensor (no downloads) and compares a model with the parent its card declares. 120,976 models so far. Some of what came out:
- 12% of declared fine-tunes hold their parent's weights unchanged.
- 23% of declared fine-tunes are LoRA-style: only the attention and MLP matrices moved, norms and embeddings untouched. That share went from 10% in 2023 to 25% in 2026.
- Of 1,668 fine-tunes named abliterated, uncensored or heretic, 53% edit only the matrices that write to the residual stream, the signature of an abliteration. The rest were trained further, merged, or are the parent's weights.
- 51% of the weight bytes on the Hub are byte-for-byte repeats of another repo's files.
Search any Hub id to see which layers and parts changed against its parent, or compare any two models.
A sample per tensor can miss things, and some formats can't be compared yet. If a verdict on a model you know looks wrong, tell me here and I'll check it against the engine. That's what this post is for, before a longer write-up.
- 2 replies
liked a
Space 1 day ago
reacted to SeaWolf-AI's post with 🔥 3 days ago
Post
4,556
No GPU. No cloud. Just your phone.
Most “small” models still beg for a datacenter. Ours fit in your pocket.
VIDRAFT POCKET is a family of GGUF models that run on an iPhone and on a GPU-free PC, straight out of the box with llama.cpp, Ollama, LM Studio, and PocketPal. Seven weeks after launch they crossed 1 million downloads. Today the family is past 2.81 million and still climbing, every day.
Three models, one pocket:
🟢 POCKET-35B — a 35B mixture-of-experts that runs on an iPhone and on a PC with no GPU. Balanced power.
→ FINAL-Bench/POCKET-35B-GGUF
🔵 POCKET-26B — a Gemma-4 based 26B MoE, tuned for on-device Korean. Lightweight and fast.
→ FINAL-Bench/POCKET-26B-GGUF
🟣 POCKET-Darwin-180B — a 180B flagship running on a laptop in 4-bit GGUF. The datacenter, on your desk.
→ FINAL-Bench/POCKET-Darwin-180B-GGUF
The whole family lives here: FINAL-Bench
Try our models live in the browser: FINAL-Bench/POCKET-Image-Studio
Private by default. Offline by design. Yours to run.
#OnDeviceAI #LocalLLM #llamacpp #GGUF #VIDRAFT #POCKET
Most “small” models still beg for a datacenter. Ours fit in your pocket.
VIDRAFT POCKET is a family of GGUF models that run on an iPhone and on a GPU-free PC, straight out of the box with llama.cpp, Ollama, LM Studio, and PocketPal. Seven weeks after launch they crossed 1 million downloads. Today the family is past 2.81 million and still climbing, every day.
Three models, one pocket:
🟢 POCKET-35B — a 35B mixture-of-experts that runs on an iPhone and on a PC with no GPU. Balanced power.
→ FINAL-Bench/POCKET-35B-GGUF
🔵 POCKET-26B — a Gemma-4 based 26B MoE, tuned for on-device Korean. Lightweight and fast.
→ FINAL-Bench/POCKET-26B-GGUF
🟣 POCKET-Darwin-180B — a 180B flagship running on a laptop in 4-bit GGUF. The datacenter, on your desk.
→ FINAL-Bench/POCKET-Darwin-180B-GGUF
The whole family lives here:
Try our models live in the browser: FINAL-Bench/POCKET-Image-Studio
Private by default. Offline by design. Yours to run.
#OnDeviceAI #LocalLLM #llamacpp #GGUF #VIDRAFT #POCKET
- 3 replies
reacted to tardellirs's post with 🔥 3 days ago
Post
5,518
I really like the LocalLLaMA/typed-decisions benchmark, so I built a small Space to make its leaderboard easier to explore: tardellirs/typed-decisions-explorer
It reads the dataset card on every visit, so new submissions show up as soon as they are merged. It has a sortable ranking and two Pareto charts: accuracy × size and accuracy × latency.
A few things it makes easy to see in the zero-shot table today:
- Only three models are within noise of the top: Quyet-1.0-Large (0.791), meraGPT Decider 1 and Baseten Mammoth 1 (0.768).
- The smallest model above Jev 1.13 (0.727) is d3-mini, at 4B.
- The fastest local run above Jev is FastBrain-9B: 50 ms on an RTX PRO 6000.
- 15 of 41 models score above the teacher's self-agreement (0.735), so the top of the table measures agreement with the teacher more than correctness.
Congrats to @codelion and @sandeep85 , the maintainers, for a great benchmark. Feedback welcome!
It reads the dataset card on every visit, so new submissions show up as soon as they are merged. It has a sortable ranking and two Pareto charts: accuracy × size and accuracy × latency.
A few things it makes easy to see in the zero-shot table today:
- Only three models are within noise of the top: Quyet-1.0-Large (0.791), meraGPT Decider 1 and Baseten Mammoth 1 (0.768).
- The smallest model above Jev 1.13 (0.727) is d3-mini, at 4B.
- The fastest local run above Jev is FastBrain-9B: 50 ms on an RTX PRO 6000.
- 15 of 41 models score above the teacher's self-agreement (0.735), so the top of the table measures agreement with the teacher more than correctness.
Congrats to @codelion and @sandeep85 , the maintainers, for a great benchmark. Feedback welcome!
- 11 replies
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Space 9 days ago