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LLaMA-Pro-8B Model Card

Model Description

LLaMA-Pro is a progressive version of the original LLaMA model, enhanced by the addition of Transformer blocks. It specializes in integrating both general language understanding and domain-specific knowledge, particularly in programming and mathematics.

Development and Training

Developed by Tencent's ARC Lab, LLaMA-Pro is an 8.3 billion parameter model. It's an expansion of LLaMA2-7B, further trained on code and math corpora totaling 80 billion tokens.

Intended Use

This model is designed for a wide range of NLP tasks, with a focus on programming, mathematics, and general language tasks. It suits scenarios requiring integration of natural and programming languages.

Performance

LLaMA-Pro demonstrates advanced performance across various benchmarks. It outperforms existing models in the LLaMA series in handling diverse tasks, showcasing its capability as an intelligent language agent.

Overall Performance on Languages, math and code tasks

Model ARC Hellaswag MMLU TruthfulQA Winogrande GSM8K GSM8K-PoT HumanEval MBPP Avg
LLAMA PRO (8B) 54.10 77.94 47.88 39.04 73.95 17.89 25.42 28.66 33.20 44.2
LLaMA2-7B 53.07 78.59 46.87 38.76 74.03 14.48 17.68 13.05 20.09 39.62
CodeLLaMA-7B 39.93 60.80 31.12 37.82 64.01 5.16 25.20 33.50 41.40 37.66
LLAMA PRO-INSTRUCT 52.30 76.88 52.57 48.80 72.53 43.59 55.61 44.51 37.88 53.8

Performance on GPT4 Evaluation

Model MT Bench
Alpaca-13B 4.53
CodeLLaMA-7B-Instruct 5.71
Vicuna-7B 6.17
LLaMA2-7B-Chat 6.27
LLAMA PRO-INSTRUCT 6.32

Limitations

While LLaMA-Pro addresses some limitations of previous models in the series, it may still encounter challenges specific to highly specialized domains or tasks.

Ethical Considerations

Users should be aware of potential biases in the model and use it responsibly, considering its impact on various applications.

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