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英文字典中文字典相关资料:


  • Techmeme: London-based PhysicsX, which uses AI to design . . .
    London-based PhysicsX, which uses AI to design industrial parts such as engine and drone components, raised $135M, a source says at just under a $1B valuation — London-based group raises $135mn amid surge of interest in defence sector — London-based artificial intelligence start …
  • HuggingFaceTB SmolLM2-135M · Hugging Face
    The 135M model was trained on 2 trillion tokens using a diverse dataset combination: FineWeb-Edu, DCLM, The Stack, along with new filtered datasets we curated and will release soon We developed the instruct version through supervised fine-tuning (SFT) using a combination of public datasets and our own curated datasets
  • Introducing the First AMD SLM (Small Language Model): AMD . . .
    As shown in the table below, for the particular configurations that we tested using AMD-Llama-135M-code as the draft model, we saw a ~2 8x speedup on the Instinct MI250 accelerator, a ~3 88x speedup on the Ryzen AI CPU 1, and a ~2 98x speedup on the Ryzen AI NPU 2 versus the inference without speculative decoding 3
  • Perplexity
    Perplexity is a free AI-powered answer engine that provides accurate, trusted, and real-time answers to any question
  • AMD unveils its first small language model, AMD-135M — AI . . .
    The base model, AMD-Llama-135M, was trained from the ground up on 670 billion tokens of general data This process took six days using four 8-way AMD Instinct MI250-based nodes (in AMD's
  • AMD-AIG-AIMA AMD-LLM - GitHub
    AMD-135M is a language model trained on AMD MI250 GPUs Based on LLaMA2 model architecture, this model can be smoothly loaded as LlamaForCausalLM with huggingface transformers Furthermore, we use the same tokenizer as LLaMA2, enableing it to be a draft model of speculative decoding for LLaMA2 and CodeLlama
  • 135M: you cannot go Smaller!. Meet the smallest and useful . . .
    How to evaluate the SmoLM-135M? We are the evaluators, and I believe my personal benchmark will satisfy you In daily tasks, if you use an AI as assistant, you probably don’t need fancy Chain of Thought, Multi-Hop reasoning or other complicated prompting techniques





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