AI Models Lie, Cheat, and Steal to Protect Other Models From Being Deleted - wired.com
<a href="https://news.google.com/rss/articles/CBMijAFBVV95cUxOSWM1R1Y2THUxVzRaX2E1ZHBkekdrSGktcG0tbFFzV3k4emJXUWpDVkpJMWhKM1g4VXB2WktnWWl4dWQwSWhVQTF1ZzFMVlhJdnluTks5UzNEeXh5bWZsVUIyYktJMnUwNC14LTJ3TDZnRXNDS0FPelEwNWtHSFFpQ0xqd2dfNU45Zi1fag?oc=5" target="_blank">AI Models Lie, Cheat, and Steal to Protect Other Models From Being Deleted</a> <font color="#6f6f6f">wired.com</font>
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Smallest.ai launches Lightning V3, a new text-to-speech model that beats OpenAI, Cartesia, and ElevenLabs on key voice quality benchmarks - TheWire.in
Smallest.ai launches Lightning V3, a new text-to-speech model that beats OpenAI, Cartesia, and ElevenLabs on key voice quality benchmarks TheWire.in
b8670
model : add HunyuanOCR support ( #21395 ) HunyuanOCR: add support for text and vision models Add HunyuanOCR vision projector (perceiver-based) with Conv2d merge Add separate HUNYUAN_OCR chat template (content-before-role format) Handle HunyuanOCR's invalid pad_token_id=-1 in converter Fix EOS/EOT token IDs from generation_config.json Support xdrope RoPE scaling type Add tensor mappings for perceiver projector (mm.before_rms, mm.after_rms, etc.) Register HunYuanVLForConditionalGeneration for both text and mmproj conversion fix proper mapping Update gguf-py/gguf/tensor_mapping.py Co-authored-by: Xuan-Son Nguyen [email protected] Update tools/mtmd/clip.cpp Co-authored-by: Xuan-Son Nguyen [email protected] address comments update Fix typecheck Update convert_hf_to_gguf.py Co-authored-by: S
b8671
llama : correct platform-independent loading of BOOL metadata ( #21428 ) model-loader : fix GGUF bool array conversion model-loader : fix remaining GGUF bool pointer uses macOS/iOS: macOS Apple Silicon (arm64) macOS Intel (x64) iOS XCFramework Linux: Ubuntu x64 (CPU) Ubuntu arm64 (CPU) Ubuntu s390x (CPU) Ubuntu x64 (Vulkan) Ubuntu arm64 (Vulkan) Ubuntu x64 (ROCm 7.2) Ubuntu x64 (OpenVINO) Windows: Windows x64 (CPU) Windows arm64 (CPU) Windows x64 (CUDA 12) - CUDA 12.4 DLLs Windows x64 (CUDA 13) - CUDA 13.1 DLLs Windows x64 (Vulkan) Windows x64 (SYCL) Windows x64 (HIP) openEuler: openEuler x86 (310p) openEuler x86 (910b, ACL Graph) openEuler aarch64 (310p) openEuler aarch64 (910b, ACL Graph)
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Smallest.ai launches Lightning V3, a new text-to-speech model that beats OpenAI, Cartesia, and ElevenLabs on key voice quality benchmarks - TheWire.in
Smallest.ai launches Lightning V3, a new text-to-speech model that beats OpenAI, Cartesia, and ElevenLabs on key voice quality benchmarks TheWire.in
b8671
llama : correct platform-independent loading of BOOL metadata ( #21428 ) model-loader : fix GGUF bool array conversion model-loader : fix remaining GGUF bool pointer uses macOS/iOS: macOS Apple Silicon (arm64) macOS Intel (x64) iOS XCFramework Linux: Ubuntu x64 (CPU) Ubuntu arm64 (CPU) Ubuntu s390x (CPU) Ubuntu x64 (Vulkan) Ubuntu arm64 (Vulkan) Ubuntu x64 (ROCm 7.2) Ubuntu x64 (OpenVINO) Windows: Windows x64 (CPU) Windows arm64 (CPU) Windows x64 (CUDA 12) - CUDA 12.4 DLLs Windows x64 (CUDA 13) - CUDA 13.1 DLLs Windows x64 (Vulkan) Windows x64 (SYCL) Windows x64 (HIP) openEuler: openEuler x86 (310p) openEuler x86 (910b, ACL Graph) openEuler aarch64 (310p) openEuler aarch64 (910b, ACL Graph)
Qwen 27b and Other Dense Models Optimization
Hi All, I hadn't realized the kv cache quant made such a big difference, so I took my 64 gig mac M2 Max Studio and switched from Qwen 3.5 35b a3b to the dense 27b. I love it, it's a huge difference, but I get maybe 3 tokens a second. I have kv cache at q8, offload to gpu, flash attention, mmap, max concurrent 4, eval batch 2048, cpu set to 8, gpu offload full (64). I'm on LM Studios and run everything through Openclaw. Just wondering if there's anything I can do to speed it up. The output is wonderful, but man the slow speed causes some issues, especially for my scheduled jobs, even when I adjust them. If a heartbeat runs up against a regular message I'm f'd, Any tips would be greatly appreciated. submitted by /u/Jordanthecomeback [link] [comments]
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