Fine-Tuning NVIDIA Nemotron to Know Local Rules, Not Just Local Languages
As AI adoption expands globally, people across languages and cultures are using it in more aspects of their daily lives. General-purpose language models serve...
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待评分 · 分类列表推荐As AI adoption expands globally, people across languages and cultures are using it in more aspects of their daily lives. General-purpose language models serve...
NVIDIA Nemotron 3.5 Transcribe and Granary v2: Intelligent Multilingual Transcription You Can Prompt and Adapt
tests : speed up tensor data generation and model saving (#31145) * tests : speed up tensor data generation and model saving Refactor save_models to create only the model, not a llama_context, which is not needed for saving. Replace the slow std::normal_distribution draw with a splitmix64 + box-muller normal generator. Assisted-by: dsh:llama.cpp/ggml-org/DeepSeek-V4-Flash-Vision-Exp-GGUF (Mac Studio, M2 Ultra, 192GB) * tests : use a shared pool of normal draws for tensor data Generate a pool of normal draws once with std::mt19937 and std::normal_distribution, then have every tensor copy a cyclic slice of it instead of running a per-tensor box-muller. The name-keyed slice selection keeps the draw deterministic. Assisted-by: dsh:llama.cpp/ggml-org/DeepSeek-V4-Flash-Vision-Exp-GGUF (Mac Studi
vulkan: fix CI llvmpipe by allowing dim 3 broadcast in MUL_MAT (#31239) * vulkan: allow dim 3 broadcast in MUL_MAT supports_op rejected every MUL_MAT whose src0 and src1 differ in ne3, although mul_mm, mul_mmq and mul_mat_vec already broadcast src0 over dim 3 through broadcast3. Such a matmul then ran on the CPU with a copy of the weight, and the split layout changed with the number of streams in the ubatch. Keep the strict check for MUL_MAT_ID only, and route the broadcast cases away from mul_mat_vec_nc, the one path that requires equal ne3. * tests : harden MUL_MAT with permuted dim 3 broadcast cases Harden the MUL_MAT coverage with a weight broadcast over dim 3 against a permuted per-stream activation, the shape of an MLA output projection with a non unified KV cache, for a single token
A government watchdog is considering a probe into the Home Office’s use of ChatGPT technology to process asylum claims amid concerns over the tools’ accuracy and secrecy surrounding their use.
There are 57 million people worldwide living with dementia today. For the researchers working to help them, the obstacle isn’t a lack of effort — it’s too much data, too few experts to synthesize it and a pace of discovery that can’t keep up with the disease. The Consortium for Biomedical Research and AI in […]
Generating tokens faster starts with identifying what limits inference performance. In the batch-size-1 evaluation of Ling-3.0-flash from Inclusion AI on NVIDIA...
Rohit Patel is departing Meta after eight years working on AI evaluations and reinforcement learning, TechCrunch has exclusively learned. His startup, Ndoc, aims to take on Microsoft Office Suite.
How to set up and run an offline coding agent using OpenCode, Ollama and Qwen.
Lots of visuals to use and learn with.
This episode of Lex Fridman was divided into 13 topics. The guests were three: Steven Brunton, who researches fluid dynamics at the University of Washington; Ankur Moitra, who works on theoretical computer science at MIT; and Alex Kontorovich, who studies number theory and Lean at Rutgers University. The video discussed the Navier–Stokes equations, the Riemann hypothesis, and computational complexity theory, prompted by AI’s claims—within just a few weeks—to have solved long-standing open problems in each of these fields. Drawing on their respective areas of expertise, the three mathematicians dissected...
mtmd-cli: Reduced scope of batch pointer (#31155) (#31165) * mtmd-cli: Reduced scope of batch pointer (#31155) * mtmd-cli: Remove CLI regression test **Website:** - **Attestations:** - **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b11932/llama-b11932-bin-macos-arm64.tar.gz) - macOS Apple Silicon (arm64, KleidiAI enabled) [DISABLED](https://github.com/ggml-org/llama.cpp/pull/23780) - [macOS Intel (x64)](https://github.com/ggml-org/llama.cpp/releases/download/b11932/llama-b11932-bin-macos-x64.tar.gz) - [iOS XCFramework](https://github.com/ggml-org/llama.cpp/releases/download/b11932/llama-b11932-xcframework.zip) **Linux:** - [Ubuntu x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b11932/llama-b11932-bin-ubuntu-x64.tar
Enterprise voice artificial intelligence company Regal Voice Inc. today added Goal Oriented Agents to its platform, enabling businesses using them to define what a call has to accomplish, and providing two AI models that split the work of getting there. The new agents are aimed at a problem Regal says shows up in calls such […] The post Regal debuts Goal Oriented Agents, putting an actor and a director model on every call appeared first on SiliconANGLE.
After the release of K3.1 Pro, due to numerous users reporting that their quotas were being depleted too quickly, Moonshot AI urgently issued an announcement stating, “The issue lies with us, and we should have fixed it sooner.” Alongside compensating affected users, Moonshot AI also indicated that it would “reduce unnecessary sub-agent calls and output lengths.” Just two days before the model went live, Anthropic published an article titled “Building agents: When to fold subagents into your main agent loop,” which addressed precisely the topic of sub-agents. Anthropic concluded that, by comparing Claude deployments across multiple companies, they found that a single agent equipped with specific skills actually outperformed “a group of...”
With an increasing number of vehicle models being designated for specific suppliers, business conditions are not necessarily improving; TianTong Weishi’s mass-production experience highlights the commercialization challenges faced by autonomous-driving system providers.
server: add router device memory margin parameter for dynamic unloading (#21231) * server: add --models-memory-max parameter to allow dynamically unloading models when they exceed a memory size threshold estimate with to-be-loaded model size included use no_alloc to get memory requirements for model load only set model memory_mb if not previously calculated use memory margin instead of total size limit, apply to each device separately add server memory debug logging move llama_context_device_memory function to llama-ext.h fix model count exceeded check improve memory_per_device map naming improve variable naming, fix style also strip models memory margin from child processes cont : clean-up replace device memory map with buft memory map. Use llama_get_memory_breakdown extract duplicated ch
Japan's GMO Internet is verifying alleged customer data posted by suspected hackers following a cloud service breach.
On October 26, Zhang Tao (online alias HideCloud), co-founder and product partner at Manus, recently published a lengthy article on his WeChat official account “Qianyun Sisi,” titled “Transforming Manus with Manus.” The piece opens with an image that left a deep impression on him. He had submitted several requirements and bug reports to an engineer; when he checked back a while later, he found that the engineer’s computer had multiple Codex and Manus sessions running side by side. Clicking on any one of them, he typed in what he had just been working on...
common : --model or -hf is required (#31242) Assisted-by: Codex **Website:** - **Attestations:** - **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b11929/llama-b11929-bin-macos-arm64.tar.gz) - macOS Apple Silicon (arm64, KleidiAI enabled) [DISABLED](https://github.com/ggml-org/llama.cpp/pull/23780) - [macOS Intel (x64)](https://github.com/ggml-org/llama.cpp/releases/download/b11929/llama-b11929-bin-macos-x64.tar.gz) - [iOS XCFramework](https://github.com/ggml-org/llama.cpp/releases/download/b11929/llama-b11929-xcframework.zip) **Linux:** - [Ubuntu x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b11929/llama-b11929-bin-ubuntu-x64.tar.gz) - [Ubuntu arm64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b1
Edge Sound Research Inc. today announced it has raised $2 million in new funding to build audio infrastructure that isolates useful sounds and combines them with other sensor data to help physical AI systems understand what’s happening in real time. Stormbreaker Ventures led the round, bringing the company’s total funding to $9 million. When the […] The post Edge Sound raises $2M to help physical AI make sense of audio appeared first on SiliconANGLE.
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