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Phi

7 updates on Phi.

  1. Confidant fine-tunes Phi2-2.7B across a phone and two laptops in 40.1 hours

    Zhejiang University researchers split a language model across three everyday devices and fine-tuned Phi2-2.7B on Alpaca in 40.1 hours without moving raw data.

  2. Amazon survey puts some small models at 10 to 100 times their parameter count

    A survey of about 160 papers finds 1B to 8B models matching much larger ones on single benchmarks, and shows where models below 1B fall apart.

  3. Microsoft runs Phi-3-mini offline on an iPhone 14 at over 12 tokens per second

    The 3.8-billion-parameter model takes about 1.8 GB at 4-bit and scores 69 percent on MMLU, which Microsoft compares to Mixtral 8x7B and GPT-3.5.

  4. TinyLLaVA's 3.1B model outscores 7B LLaVA-1.5 on seven of nine benchmarks

    Beihang and Tsinghua researchers report a 3.1B vision-language model that beats the 7B LLaVA-1.5 on seven of nine image benchmarks.

  5. Microsoft releases Phi-2, a 2.7B model it says matches models 25 times larger

    The 2.7B base model was trained on 1.4 trillion tokens in 14 days on 96 A100 GPUs, and Microsoft says it matches models up to 25 times larger.

  6. Microsoft carries its textbook data recipe from code to reasoning with the 1.3B phi-1.5

    The 1.3-billion-parameter model trains on 30B tokens of mostly synthetic data and posts reasoning scores above Llama2-7B in Microsoft evaluations.

  7. Microsoft trains phi-1 to 50.6 percent on HumanEval with 1.3B parameters

    Microsoft Research reports 50.6 percent on HumanEval and 55.5 percent on MBPP for a 1.3B code model trained on under 7B tokens of textbook-quality data.