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Tom K.

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reacted to eaddario's post with 🔥 about 1 month ago
Experimental global target bits‑per‑weight quantization of Qwen/Qwen3.6-27B and Qwen/Qwen3.6-35B-A3B. Unlike standard llama.cpp quantizations that rely on fixed type heuristics (e.g., Q4_K_M), the Target BPW approach optimizes per-tensor precision where it matters the most, and produces high quality models that meet a precise global file size target. Key Advantages: - VRAM Maximization: Can generate high quality models sized exactly to fit hardware constraints (e.g., fitting the model into exactly 24GB VRAM). - Data-Driven Precision: Quantization mix is determined by actual weight error sensitivity rather than hardcoded rules, often yielding better PPL/KLD size trade-offs. Full benchmarks (PPL, KLD, ARC, GPQA, MMLU, etc.) and methodology in the models' cards. https://huggingface.co/eaddario/Qwen3.6-27B-GGUF https://huggingface.co/eaddario/Qwen3.6-35B-A3B-GGUF
reacted to kelsend's post with 👀 about 1 month ago
The rebuilt Hunyuan HY3 Preview is here! I tested it on all the tricky scenarios where most LLMs usually face-plant—and guess what? It didn’t flop. 295B total params, 21B active params, 256K context window. Built on MoE architecture, it delivers trillion-parameter-level performance with a much smaller footprint. Long-context capabilities get a massive upgrade. Agent abilities stand out this time: tool calling, workflow orchestration, and autonomous planning are far more stable in real business scenarios. AI PPT generation in Tencent Docs is also significantly smoother and more reliable. Real-world tests on WorkBuddy show first-token latency down 54%, success rate over 99.99%, and an Agent workflow that ran continuously for 495 steps. Its Coding Agent achieved top-tier results on both SWE-Bench Verified and Terminal-Bench 2.0 Now open-sourced on GitHub, HuggingFace, and ModelScope. Available on TokenHub at just 1.2 RMB per million tokens.
liked a model about 1 month ago
deepseek-ai/DeepSeek-V4-Pro
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