alkinun

AtAndDev

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https://baretile.com/

alkinun

AI & ML interests

decentralize

Recent Activity

reacted to savoji's post with 🔥 about 5 hours ago

Idol, a new Apache-2.0 image-to-3D model from @speridlabs, makes a 3D Gaussian splat from one image in 7.2 GB of peak VRAM. It is the #1 trending model on the Hub with 1,057 likes. It writes a standard 3DGS .ply with up to 1,048,576 Gaussians, including sides the picture never showed, and does background removal and framing itself. The catch: the only quality evidence is the authors' own benchmark, on a repo created 2026-10-28. 📊 The numbers • Authors' benchmark, 1 x H100: best in 6 of 8 columns vs TripoSplat, TRELLIS and LGM (SSIM 0.726, LPIPS 0.213, DINOv2 0.781, CLIP 0.885) • LGM wins PSNR (12.46 vs 12.25) and speed (1.1 s). Idol takes 6.8 s vs 7.1 s for TRELLIS and 9.9 s for TripoSplat • 262,144 Gaussians, 50 steps: 3.4 s on B300, 6.7 s on H100, 19.1 s on RTX 4090, 46.2 s on a 10 GB RTX 3080 🛠️ Try it • Install idol-sdk, accept the terms of the gated DINOv3 and FLUX.2-dev models, run hf auth login • python -m idol.sample --image photo.jpg --mode real • --num_gaussians runs from 32,768 (0.4 MB) to 1,048,576 (12.2 MB) ⚠️ Caveats • Test set and size are not named, and no independent evaluation exists yet • The card says "under 2 seconds", but the timing table shows 3.4 s at best • Single objects only, NVIDIA CUDA required, no failure cases shown https://huggingface.co/speridlabs/idol https://speridlabs.com/research/idol If you run it on your own messy photos, how do the unseen sides hold up? 📚 Read it elsewhere • The deep dive on Medium: https://medium.com/p/5f660ae6ae56 • The long read on Substack: https://sajjadpakdamansavoji.substack.com/p/speridlabs-idol-builds-3d-splats • The thread on X: https://x.com/saavoji/status/2119137739580858545 • Every version on sajjadpsavoji.github.io: https://sajjadpsavoji.github.io/blog#speridlabs-idol

liked a Space about 8 hours ago

FineEnvs/RL-Explorer

liked a dataset about 8 hours ago

allenai/Dolci-Think-SFT-Clean-v2

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reacted to savoji's post with 🔥 about 5 hours ago

Post

405

Idol, a new Apache-2.0 image-to-3D model from @speridlabs , makes a 3D Gaussian splat from one image in 7.2 GB of peak VRAM. It is the #1 trending model on the Hub with 1,057 likes.

It writes a standard 3DGS .ply with up to 1,048,576 Gaussians, including sides the picture never showed, and does background removal and framing itself. The catch: the only quality evidence is the authors' own benchmark, on a repo created 2026-10-28.

📊 The numbers
• Authors' benchmark, 1 x H100: best in 6 of 8 columns vs TripoSplat, TRELLIS and LGM (SSIM 0.726, LPIPS 0.213, DINOv2 0.781, CLIP 0.885)
• LGM wins PSNR (12.46 vs 12.25) and speed (1.1 s). Idol takes 6.8 s vs 7.1 s for TRELLIS and 9.9 s for TripoSplat
• 262,144 Gaussians, 50 steps: 3.4 s on B300, 6.7 s on H100, 19.1 s on RTX 4090, 46.2 s on a 10 GB RTX 3080

🛠️ Try it
• Install idol-sdk, accept the terms of the gated DINOv3 and FLUX.2-dev models, run hf auth login
• python -m idol.sample --image photo.jpg --mode real
• --num_gaussians runs from 32,768 (0.4 MB) to 1,048,576 (12.2 MB)

⚠️ Caveats
• Test set and size are not named, and no independent evaluation exists yet
• The card says "under 2 seconds", but the timing table shows 3.4 s at best
• Single objects only, NVIDIA CUDA required, no failure cases shown

speridlabs/idol
https://speridlabs.com/research/idol

If you run it on your own messy photos, how do the unseen sides hold up?

📚 Read it elsewhere
• The deep dive on Medium: https://medium.com/p/5f660ae6ae56
• The long read on Substack: https://sajjadpakdamansavoji.substack.com/p/speridlabs-idol-builds-3d-splats
• The thread on X: https://x.com/saavoji/status/2119137739580858545
• Every version on sajjadpsavoji.github.io: https://sajjadpsavoji.github.io/blog#speridlabs-idol

reacted to danielhanchen's post with ❤️🚀🔥 about 9 hours ago

Post

1,838

Qwen3.8-Flash-Next GGUFs now run faster with less thinking and less token use.

Unsloth Dynamic V3.1 GGUFs bring thinking & token use to match 95% of full precision Qwen3.8-Flash.

Results: 50% fewer tokens + 15% higher accuracy vs same size quants.

GGUF: unsloth/Qwen3.8-Flash-Next-GGUF
Analysis/Guide: https://unsloth.ai/docs/models/qwen3.8-next

reacted to SeaWolf-AI's post with 🔥 1 day ago

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8,026

SuperCLUE’s November 2026 results highlight why category scores matter when choosing a model.

Across the 19 published entries, here are the highest displayed scores in each category, followed by the leader among models labeled open:

• Agentic coding: GPT-6.1-Sol (69.23); open: DeepSeek-V4.1-Flash-1017 (65.38).
• Mathematical and scientific reasoning: GPT-6.1-Sol (76.32); open: Darwin-180B-RSI-R3 (73.68).
• Hallucination control: Gemini-4-Argon (83.19); open: Kimi-K3.1-Pro (67.78).
• Precise instruction following: Gemini-4-Argon and GPT-6.1-Sol (68.97 each); open: Kimi-K3.1-Pro (44.83).
• Ultra-long context: Gemini-4-Argon (95.63); open: Kimi-K3.1-Pro (87.25).

On the overall leaderboard, Gemini-4-Argon (73.62) and GPT-6.1-Sol (71.76) have the highest displayed scores, but neither has an official rank number. Kimi-K3.1-Pro (61.46) holds official rank No. 1 and leads the open models overall, followed by DeepSeek-V4.1-Flash-1017 (58.95, official No. 2) and GLM-5.3 (50.66, official No. 3).

These comparisons include entries whose official rank is “-”. Among officially ranked open models only, DeepSeek leads mathematical and scientific reasoning at 71.05. “Open” follows SuperCLUE’s classification, not an independent license review. Scores are benchmark points, not error rates; small differences and results from a Chinese-language-focused evaluation need validation on your own workloads. Detailed evaluation settings and the aggregation formula were not verified for this summary.

Read the full breakdown: https://huggingface.co/blog/FINAL-Bench/superclue-november-2026-category-leaders-open-mode

Source: SuperCLUE results released November 5, 2026
https://www.superclueai.com/homepage
Official November data: https://www.superclueai.com/data/generalboard/2026%E5%B9%B411%E6%9C%88.xlsx

reacted to Quazim0t0's post with 🔥 2 days ago

Post

7,335

Quazim0t0/Qwenx86-4B-compact

▶️ See it in action: Quazim0t0/Qwenx86-Debugger . Walk through actual recorded tool calls: the conversation, x86 registers and flags, code at the program counter, RAM and machine output, with all verified blocks on a timeline.

Qwen3.5-4B with a 64-bit x86 computer compiled into its weights: 13 transformer layers ahead of Qwen's own. There are no learned weights in them; all of their weights are computed by a compiler. Qwen invokes the computer through its own tool calls (<tool_call>), and the computer in the weights executes the tool. Calculator, Python, C compiler, notes store, physics/chemistry lookup and Python-docs lookup are all firmware in the checkpoint. This is the compact checkpoint: ~9 GB GPU memory, so it fits on a 16 GB GPU.

Edit: GGUF Release: Quazim0t0/Qwenx86-4B-GGUF

Credits: Qwenputer by Divyansh Agrawal (@divagr1925 , github.com/divagr18/qwenputer, MIT)

1 reply

reacted to appvoid's post with 🔥👀❤️ 2 days ago

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7,555

New leaderboard coming to the small language model community!

The leaderboard measures cognitive skills, which is more challenging and beyond language modeling and instruction-following. This is a first step towards more everyday useful and general small models.

dotlabs/cognirank

2 replies

reacted to danielhanchen's post with 😎👍🚀❤️🔥 3 days ago

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7,106

We’re releasing new Qwen3.8-Flash-Next GGUFs with 15% higher accuracy.

Unsloth Dynamic V3.1 outperforms others by >15% on Terminal Bench, DeepSWE & more benchmarks (256K context).

Run on 32GB+ RAM setups.

GGUF: unsloth/Qwen3.8-Flash-Next-GGUF
Blog: https://unsloth.ai/docs/models/qwen3.8-next

reacted to savoji's post with 🚀 4 days ago

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4,852

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