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license: llama2 |
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# LLaMA-Pro-8B Model Card |
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## Model Description |
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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. |
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## Development and Training |
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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. |
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## Intended Use |
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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. |
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## Performance |
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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. |
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### Overall Performance on Languages, math and code tasks |
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| Model | ARC | Hellaswag | MMLU | TruthfulQA | Winogrande | GSM8K | GSM8K-PoT | HumanEval | MBPP | Avg | |
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| :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | |
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| LLAMA PRO (8B) | 54.10 | 77.94 | 47.88 | 39.04 | 73.95 | 17.89 | 25.42 | 28.66 | 33.20 | 44.2 | |
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| LLaMA2-7B | 53.07 | 78.59 | 46.87 | 38.76 | 74.03 | 14.48 | 17.68 | 13.05 | 20.09 | 39.62 | |
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| CodeLLaMA-7B | 39.93 | 60.80 | 31.12 | 37.82 | 64.01 | 5.16 | 25.20 | 33.50 | 41.40 | 37.66 | |
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| LLAMA PRO-INSTRUCT | 52.30 | 76.88 | 52.57 | 48.80 | 72.53 | 43.59 | 55.61 | 44.51 | 37.88 | 53.8 | |
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### Performance on GPT4 Evaluation |
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| Model | MT Bench | |
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| :-: | :-: | |
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| Alpaca-13B | 4.53 | |
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| CodeLLaMA-7B-Instruct | 5.71 | |
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| Vicuna-7B | 6.17 | |
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| LLaMA2-7B-Chat | 6.27 | |
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| LLAMA PRO-INSTRUCT | 6.32 | |
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## Limitations |
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While LLaMA-Pro addresses some limitations of previous models in the series, it may still encounter challenges specific to highly specialized domains or tasks. |
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## Ethical Considerations |
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Users should be aware of potential biases in the model and use it responsibly, considering its impact on various applications. |
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