Je viens de gagner 7 500 XP sur la Saison 1 de @pokeitem_ 🏆 Télécharge l'app avec mon lien de parrainage : pokeitem.fr/r/dan_ton
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Le cheminement me semble assez clair avec cette décision du gouvernement U.S. de restreindre l'accès à Fable 5 aux non-americains. Je parie que la suite sera un accord avec Anthropic mais avec obligation de KYC pour les utilisateurs....
À côté de ça, les modèles open weights vont se perfectionner, et le fossé de fiabilité entre les IA propriétaires et open source va forcément diminuer. Et là on dit merci à la Chine
Interesting result from a quick local test on Qwen3.6-27B from @Alibaba_Qwen
Same model, same prompt, same file, same machine (RTX 4090).
I just ran 2 servers:
reasoning OFF
reasoning ON
On a small code-fix task with Hermes Agent (@NousResearch), the reasoning ON run gave me the cleaner output.
Didn’t expect that, especially since with earlier Qwen runs I usually saw more drift when reasoning was enabled.
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DanTon retweeted
Qwen3.5 27B vs Gemma4 31B | Canvas Creativity Test
Why HTML Canvas? Two reasons:
1. It's unforgiving, one small mistake and the whole thing breaks
2. We kept prompts short to test real creativity, not instruction following
4 rounds:
- Analog Clock
- Hyperspace Tunnel
- Growing Tree
- Black Hole
Both nailed the clock, but the other three is where it gets interesting.
Looking forward to Qwen3.6 open-weight release!
Yesterday @Google released Gemma 4 31B and pushed even harder into the local model on a high-end consumer GPU lane.
Now @Alibaba_Qwen is already teasing smaller open Qwen3.6 releases, and if this poll is any indication, 27B is the one people want first.
Qwen3.5-27B is still one of the local staples, so a Qwen3.6-27B could get very interesting very fast.
The pace of local AI right now is honestly insane. What a time to be building
We are planning to open-source the Qwen3.6 models (particularly medium-sized versions) to facilitate local deployment and customization for developers. Please vote for the model size you are **most** anticipating—the community’s voice is vital to us!
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Announcement of the day: Gemma 4. I’m not home right now so I can’t test Gemma 4 myself yet, so this is a benchmark-based read. And yeah, the 31B is clearly stepping into Qwen3.5-27B territory for people running local models on a single high-end 24GB GPU.
Looking at the official benchmark sheets, Qwen still has a small edge: 86.1 vs 85.2 on MMLU-Pro, 85.5 vs 84.3 on GPQA Diamond, and 80.7 vs 80.0 on LiveCodeBench v6.
So far, Qwen looks stronger on structured reasoning and coding benchmarks. Gemma 4 31B, on the other hand, seems to have the edge in general chat preference right now, and it also comes with native multimodality.
Arena leans that way too: Gemma 4 31B sits at 1452, Qwen3.5-27B at 1404.
So IMO the simple read is: Qwen still looks a bit cleaner on paper, a lot more mature as a local model stack right now, and probably the more stable and efficient fit on a 24GB VRAM setup. But Gemma 4 31B is absolutely a real challenger.
We just released Gemma 4 — our most intelligent open models to date.
Built from the same world-class research as Gemini 3, Gemma 4 brings breakthrough intelligence directly to your own hardware for advanced reasoning and agentic workflows.
Released under a commercially permissive Apache 2.0 license so anyone can build powerful AI tools. 🧵↓
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Two years ago I built this rig mostly for gaming. Today it’s building itself with @NousResearch’s Hermes Agent and @Alibaba_Qwen’s Qwen3.5-35B-A3B. After just two days, using Hermes in a strict and tightly scoped way, the results are already far beyond my expectations
#BlackMythWukong with RTX 4090, 7950x3d and 64go DDR5. Full Ray tracing and cinematics settings on OLED monitor. Really beautiful
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DanTon retweeted
Introducing Unsloth Studio ✨
A new open-source web UI to train and run LLMs.
• Run models locally on Mac, Windows, Linux
• Train 500+ models 2x faster with 70% less VRAM
• Supports GGUF, vision, audio, embedding models
• Auto-create datasets from PDF, CSV, DOCX
• Self-healing tool calling and code execution
• Compare models side by side + export to GGUF
GitHub: github.com/unslothai/unsloth
Blog and Guide: unsloth.ai/docs/new/studio
Available now on Hugging Face, NVIDIA, Docker and Colab.