Swiss Locomotive and Machine Works
PulseAugur coverage of Swiss Locomotive and Machine Works — every cluster mentioning Swiss Locomotive and Machine Works across labs, papers, and developer communities, ranked by signal.
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Dataset preparation is key to successful model fine-tuning
This article emphasizes the critical importance of dataset preparation before engaging in model fine-tuning. It details how a well-structured and relevant dataset is foundational for successful fine-tuning, regardless o…
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MosChip showcases edge AI and voice chatbot at Embedded Vision Summit
MosChip is showcasing an edge-intelligence demo and a secure, voice-processed AI chatbot at the Embedded Vision Summit 2026. The company is exhibiting at Cadence's booth, number 402, to demonstrate their capabilities in…
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TextPro-SLM reduces speech LLM modality gap by enhancing input processing
Researchers have developed TextPro-SLM, a novel speech large language model (SLM) designed to minimize the modality gap between spoken and text-based inputs. Unlike previous approaches focusing on output generation, Tex…
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GRAIL framework slashes agent discovery latency by 79x with SLM-enhanced indexing
Researchers have developed GRAIL, a new framework designed to significantly speed up the discovery of AI agents for multi-agent collaboration. GRAIL utilizes a specialized Small Language Model (SLM) for faster capabilit…
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Free Pascal and BLAS offer deterministic HPC for SLM projects
A user on Mastodon is exploring the use of BLAS, a set of Fortran-based matrix routines, for their Small Language Model (SLM) project. They express a preference for Free Pascal over languages like C, Python, Rust, and C…
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Free Pascal and BLAS offer faster matrix multiplication for AI development
A user explored the performance of Python for AI tasks, noting its slowness but acknowledging the extensive AI ecosystem as its primary advantage. They conducted a test comparing Free Pascal and BLAS for matrix multipli…
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RadLite fine-tunes small LLMs for CPU-deployable radiology AI
Researchers have developed RadLite, a method for fine-tuning small language models (SLMs) with 3-4 billion parameters for radiology tasks. This approach, utilizing LoRA fine-tuning on models like Qwen2.5-3B-Instruct and…
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New 'Select to Think' method boosts small language models' reasoning
Researchers have developed a new method called Select to Think (S2T) to improve the reasoning capabilities of small language models (SLMs). S2T addresses the limitations of SLMs by reframing the role of larger language …
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Small LMs achieve better reasoning with budget-aware guidance and prompt disambiguation
Researchers are exploring methods to enhance the reasoning capabilities of smaller language models (SLMs) without increasing their size or computational cost. One approach focuses on pre-inference prompt disambiguation,…
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New research boosts LLM reasoning with speculative methods and physical insights
Recent research explores novel methods to enhance the reasoning capabilities and efficiency of large language models (LLMs). Papers introduce techniques like speculative exploration for Tree-of-Thought reasoning to brea…