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--- |
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license: apache-2.0 |
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language: |
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- en |
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pipeline_tag: text-generation |
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tags: |
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- MoE |
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--- |
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# LLaMA-MoE-v1-3.5B (4/16) |
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[[💻 Code]](https://github.com/pjlab-sys4nlp/llama-moe) | [[📜 Technical Report]](https://github.com/pjlab-sys4nlp/llama-moe/blob/main/docs/LLaMA_MoE.pdf) |
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👋 Very nice to meet you here~ |
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❤️ This repo contains the model `LLaMA-MoE-v1-3.5B (4/16)`, which activates 4 out of 16 experts (3.5B parameters). |
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This model is NOT fine-tuned by instruction pairs, so it may not be good enough to act like a chatbot. |
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📢 LLaMA-MoE is a series of Mixture-of-Expert (MoE) models based on [LLaMA-2](https://huggingface.co/meta-llama/Llama-2-7b-hf). |
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You can find the code for training this model at [this repo](https://github.com/pjlab-sys4nlp/llama-moe). |
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💎 This series of models are obtained by partitioning original LLaMA FFNs into experts and further continual pre-training. |
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The total model size is only 6.7B parameters, which is very convenient for deployment and research usage. |
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More details could be found at [our technical report](https://arxiv.org/). |
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## 🚀 QuickStart |
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```python |
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# python>=3.10 |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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model_dir = "llama-moe/LLaMA-MoE-v1-3_5B-4_16" |
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tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained(model_dir, torch_dtype=torch.bfloat16, trust_remote_code=True) |
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model.eval() |
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model.to("cuda:0") |
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input_text = "Suzhou is famous of" |
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inputs = tokenizer(input_text, return_tensors="pt") |
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inputs = inputs.to("cuda:0") |
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pred = model.generate(**inputs, max_length=50, temperature=0.0) |
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print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True)) |
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# Suzhou is famous of its beautiful gardens. The most famous one is the Humble Administrator's Garden. It is a classical Chinese garden with a history of more than 600 years. The garden is divided into three |
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``` |
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## 📊 Performance |
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| Model | \#Activated Experts | \#Experts | \#Activated Params | Links | |
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| :------------------------ | :-----------------: | :-------: | :----------------: | :-----------------------------------------------------------------------: | |
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| **LLaMA-MoE-3.0B** | 2 | 16 | 3.0B | [[🤗 HF Weights]](https://huggingface.co/llama-moe/LLaMA-MoE-v1-3_0B-2_16) | |
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| **LLaMA-MoE-3.5B (4/16)** | 4 | 16 | 3.5B | [[🤗 HF Weights]](https://huggingface.co/llama-moe/LLaMA-MoE-v1-3_5B-4_16) | |
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| **LLaMA-MoE-3.5B (2/8)** | 2 | 8 | 3.5B | [[🤗 HF Weights]](https://huggingface.co/llama-moe/LLaMA-MoE-v1-3_5B-2_8) | |
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| Model | SciQ | PIQA | WinoGrande | ARC-e | ARC-c (25) | HellaSwag (10) | LogiQA | BoolQ (32) | LAMBADA | NQ (32) | MMLU (5) | Average | |
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| :------------------------------------------------------------------------------------ | :------: | :------: | :--------: | :------: | :--------: | :------------: | :------: | :--------: | :------: | :------: | :-------: | :-----: | |
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| [OPT-2.7B](https://huggingface.co/facebook/opt-2.7b) | 78.9 | 74.8 | 60.8 | 54.4 | 34.0 | 61.4 | 25.8 | 63.3 | 63.6 | 10.7 | 25.8 | 50.3 | |
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| [Pythia-2.8B](https://huggingface.co/EleutherAI/pythia-2.8b) | 83.2 | 73.6 | 59.6 | 58.8 | 36.7 | 60.7 | 28.1 | 65.9 | 64.6 | 8.7 | 26.8 | 51.5 | |
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| [INCITE-BASE-3B](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-3B-v1) | 85.6 | 73.9 | 63.5 | 61.7 | 40.3 | 64.7 | 27.5 | 65.8 | 65.4 | 15.2 | 27.2 | 53.7 | |
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| [Open-LLaMA-3B-v2](https://huggingface.co/openlm-research/open_llama_3b_v2) | 88.0 | 77.9 | 63.1 | 63.3 | 40.1 | 71.4 | 28.1 | 69.2 | 67.4 | 16.0 | 26.8 | 55.6 | |
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| [Sheared-LLaMA-2.7B](https://huggingface.co/princeton-nlp/Sheared-LLaMA-2.7B) | 87.5 | 76.9 | 65.0 | 63.3 | 41.6 | 71.0 | 28.3 | 73.6 | 68.3 | 17.6 | **27.3** | 56.4 | |
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| **LLaMA-MoE-3.0B** | 84.2 | 77.5 | 63.6 | 60.2 | 40.9 | 70.8 | **30.6** | 71.9 | 66.6 | 17.0 | 26.8 | 55.5 | |
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| **LLaMA-MoE-3.5B (4/16)** | 87.6 | **77.9** | 65.5 | **65.6** | **44.2** | **73.3** | 29.7 | **75.0** | **69.5** | **20.3** | 26.8 | 57.7 | |
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| **LLaMA-MoE-3.5B (2/8)** | **88.4** | 77.6 | **66.7** | 65.3 | 43.1 | **73.3** | 29.6 | 73.9 | 69.4 | 19.8 | 27.0 | 57.6 | |
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## 📖 Details |
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Training Data: 200B tokens from [SlimPajama](https://www.cerebras.net/blog/slimpajama-a-627b-token-cleaned-and-deduplicated-version-of-redpajama) with the same data sampling weights as [Sheared LLaMA](https://arxiv.org/abs/2310.06694). |
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## 📃 Citation |
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```bibtex |
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@article{llama-moe, |
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title={LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre-training}, |
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author={Tong Zhu and Xiaoye Qu and Daize Dong and Jiacheng Ruan and Jingqi Tong and Conghui He and Yu Cheng}, |
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journal={arXiv preprint arXiv:2406.16554}, |
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year={2024}, |
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url={https://arxiv.org/abs/2406.16554}, |
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} |
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``` |
