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Found 4 papers, 1 papers with code
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
We introduce phi-3-mini, a 3. 8 billion parameter language model trained on 3. 3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3. 5 (e. g., phi-3-mini achieves 69% on MMLU and 8. 38 on MT-bench), despite being small enough to be deployed on a phone.
Ranked #5 on
MMR total
on MRR-Benchmark
(using extra training data)
Textbooks Are All You Need II: phi-1.5 technical report
We continue the investigation into the power of smaller Transformer-based language models as initiated by \textbf{TinyStories} -- a 10 million parameter model that can produce coherent English -- and the follow-up work on \textbf{phi-1}, a 1. 3 billion parameter model with Python coding performance close to the state-of-the-art.
Ranked #16 on
Question Answering
on SIQA
SkipDecode: Autoregressive Skip Decoding with Batching and Caching for Efficient LLM Inference
While existing token-level early exit methods show promising results for online inference, they cannot be readily applied for batch inferencing and Key-Value caching.
Textbooks Are All You Need
Despite this small scale, phi-1 attains pass@1 accuracy 50. 6% on HumanEval and 55. 5% on MBPP.
Ranked #56 on
Code Generation
on HumanEval
