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We are working on LFM3-1.7B-Ox, a compact model for repo-level programming, agentic coding, and tinkering.
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alkinun
AtAndDev
AI & ML interests
decentralize
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reacted to DedeProGames's post with 🔥 about 4 hours ago
We are working on LFM3-1.7B-Ox, a compact model for repo-level programming, agentic coding, and tinkering. reacted to Banaxi-Tech's post with 🔥 about 14 hours ago
We're introducing KV1 1.2B.
Its our first KV1 model using a 2 bit KV cache, based on LFM 2.5 1.2B.
KV1 1.2B retains 75.8% of original intelligence.
More details: https://huggingface.co/spaces/klein-ml/blog#kv1-1.2b
Model https://huggingface.co/klein-ml/KV1-1.2B
reacted to Banaxi-Tech's post with 🔥 1 day ago
BananaMind 3 is coming.
Biggest context in all SLMs with NSA.
10M, 50M, 150M, 400M.
And maybe, 1B.Organizations
reacted to DedeProGames's post with 🔥 about 4 hours ago
reacted to Banaxi-Tech's post with 🔥 about 14 hours ago
Post
2,333
We're introducing KV1 1.2B.
Its our first KV1 model using a 2 bit KV cache, based on LFM 2.5 1.2B.
KV1 1.2B retains 75.8% of original intelligence.
More details: klein-ml/blog
Model klein-ml/KV1-1.2B
Its our first KV1 model using a 2 bit KV cache, based on LFM 2.5 1.2B.
KV1 1.2B retains 75.8% of original intelligence.
More details: klein-ml/blog
Model klein-ml/KV1-1.2B
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reacted to Banaxi-Tech's post with 🔥 1 day ago
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6,139
BananaMind 3 is coming.
Biggest context in all SLMs with NSA.
10M, 50M, 150M, 400M.
And maybe, 1B.
Biggest context in all SLMs with NSA.
10M, 50M, 150M, 400M.
And maybe, 1B.
- 14 replies
reacted to SeaWolf-AI's post with 🔥🚀 1 day ago
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6,501
🌦️ Which weather model is most accurate? We score them every day, in the open.
We released two things on Hugging Face:
🏆 Global Weather Model Leaderboard
Every day it records what each global model predicted for 24 world cities, compares it with what actually happened (ERA5), and updates the ranking. ECMWF IFS and AIFS, NOAA GFS and AIGFS, DWD ICON, JMA, ECCC GEM, MET Norway, side by side, on the same cases, since 1 January 2026.
Four elements, four tabs: max temperature, min temperature, wind, precipitation. Forecast leads from 1 to 7 days, with 8 to 10 days joining as they are verified.
🧬 Darwin-Prediction-Weather
VIDRAFT's weather prediction system: a fusion engine that combines global forecasts, learning only from days already observed. It holds the lowest error in all four elements at every lead on the 2026 record, and the lowest error in all 15 evaluated cells of the WeatherBench 2 (2020) protocol.
What the 2026 record shows so far:
• AI models now lead physics models at the major centres (AIFS vs IFS, AIGFS vs GFS)
• Fusion goes further: 7-day max temperature RMSE 2.09 °C vs 2.41 °C for the next model
Also inside: yesterday's results with a daily WIN for the closest model, a 10-day outlook for every city, champions by city and by month, and a Live Earth map with wind, clouds, radar and temperature.
Every forecast and every observed value is open as CSV, so anyone can check the scores.
📖 Article: https://huggingface.co/blog/FINAL-Bench/which-weather-model-is-most-accurate-we-score-them
🏆 Leaderboard: FINAL-Bench/Weather-Model-Leaderboard
🧬 Model: FINAL-Bench/Darwin-Prediction-Weather
📊 Daily record: FINAL-Bench/Weather-Model-Forecast-Archive
Questions, corrections and new models to add are welcome.
We released two things on Hugging Face:
🏆 Global Weather Model Leaderboard
Every day it records what each global model predicted for 24 world cities, compares it with what actually happened (ERA5), and updates the ranking. ECMWF IFS and AIFS, NOAA GFS and AIGFS, DWD ICON, JMA, ECCC GEM, MET Norway, side by side, on the same cases, since 1 January 2026.
Four elements, four tabs: max temperature, min temperature, wind, precipitation. Forecast leads from 1 to 7 days, with 8 to 10 days joining as they are verified.
🧬 Darwin-Prediction-Weather
VIDRAFT's weather prediction system: a fusion engine that combines global forecasts, learning only from days already observed. It holds the lowest error in all four elements at every lead on the 2026 record, and the lowest error in all 15 evaluated cells of the WeatherBench 2 (2020) protocol.
What the 2026 record shows so far:
• AI models now lead physics models at the major centres (AIFS vs IFS, AIGFS vs GFS)
• Fusion goes further: 7-day max temperature RMSE 2.09 °C vs 2.41 °C for the next model
Also inside: yesterday's results with a daily WIN for the closest model, a 10-day outlook for every city, champions by city and by month, and a Live Earth map with wind, clouds, radar and temperature.
Every forecast and every observed value is open as CSV, so anyone can check the scores.
📖 Article: https://huggingface.co/blog/FINAL-Bench/which-weather-model-is-most-accurate-we-score-them
🏆 Leaderboard: FINAL-Bench/Weather-Model-Leaderboard
🧬 Model: FINAL-Bench/Darwin-Prediction-Weather
📊 Daily record: FINAL-Bench/Weather-Model-Forecast-Archive
Questions, corrections and new models to add are welcome.
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reacted to sergiopaniego's post with 🚀🔥 5 days ago
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New post-training example: SFT on agent traces with TRL
→ A large model works as a coding agent in OpenCode, run through OpenEnv
→ The tasks it solves become the training data
→ A 2B model goes from 5.5% to 34.4% on held-out tasks
About 500 traces get most of the gain. Five times more add only 3 to 4 points, so SFT gets the agent working and RL has to do the rest
Full guide, with the models, traces and every rollout open:
https://github.com/huggingface/trl/tree/main/examples/sft_agent_traces
→ A large model works as a coding agent in OpenCode, run through OpenEnv
→ The tasks it solves become the training data
→ A 2B model goes from 5.5% to 34.4% on held-out tasks
About 500 traces get most of the gain. Five times more add only 3 to 4 points, so SFT gets the agent working and RL has to do the rest
Full guide, with the models, traces and every rollout open:
https://github.com/huggingface/trl/tree/main/examples/sft_agent_traces
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liked a
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replied to Banaxi-Tech's post 6 days ago
oh nice! well, good luck. very excited.
replied to Banaxi-Tech's post 6 days ago
cool, but what happened to the promise? :)
reacted to Banaxi-Tech's post with 🔥 6 days ago
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We're excited to release BananaMind 2.5 Pro.
It's done some post-training on our BananaMind 2 Pro base, via distillation of SmolLM2 360M.
It scores 28.98 INT INDEX compared to 24.96 of BananaMind 2 Pro on the AxiomicLabs/Open_SLM_Leaderboard
Check it out at BananaMind/BananaMind-2.5-Pro
BananaMind
@ArushCodes
@Bc-AI
It's done some post-training on our BananaMind 2 Pro base, via distillation of SmolLM2 360M.
It scores 28.98 INT INDEX compared to 24.96 of BananaMind 2 Pro on the AxiomicLabs/Open_SLM_Leaderboard
Check it out at BananaMind/BananaMind-2.5-Pro
@ArushCodes
@Bc-AI
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reacted to savoji's post with 🔥 7 days ago
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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
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
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