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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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tags: |
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- MoE |
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--- |
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# LLaMA-MoE-v1-3.0B (2/16) SFT |
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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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This is the supervised fine-tuned version of [LLaMA-MoE-v1-3_0B-2_16](https://huggingface.co/llama-moe/LLaMA-MoE-v1-3_0B-2_16) on [Deita-6k](https://huggingface.co/datasets/hkust-nlp/deita-6k-v0) for 2 epochs. |
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| Model | \#Activated Experts | \#Experts | \#Activated Params | Foundation Model | SFT Model | |
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| :------------------------ | :-----------------: | :-------: | :----------------: | :---------------------------------------------------------------: | :------------------------------------------------------------------: | |
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| **LLaMA-MoE-3.0B** | 2 | 16 | 3.0B | [🤗 base](https://huggingface.co/llama-moe/LLaMA-MoE-v1-3_0B-2_16) | [🤗 SFT](https://huggingface.co/llama-moe/LLaMA-MoE-v1-3_0B-2_16-sft) | |
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| **LLaMA-MoE-3.5B (4/16)** | 4 | 16 | 3.5B | [🤗 base](https://huggingface.co/llama-moe/LLaMA-MoE-v1-3_5B-4_16) | [🤗 SFT](https://huggingface.co/llama-moe/LLaMA-MoE-v1-3_5B-4_16-sft) | |
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| **LLaMA-MoE-3.5B (2/8)** | 2 | 8 | 3.5B | [🤗 base](https://huggingface.co/llama-moe/LLaMA-MoE-v1-3_5B-2_8) | [🤗 SFT](https://huggingface.co/llama-moe/LLaMA-MoE-v1-3_5B-2_8-sft) | |
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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_0B-2_16-sft" |
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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.cuda() |
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input_text = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. human: Give me a three-day plan in Suzhou. gpt:" |
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inputs = tokenizer(input_text, return_tensors="pt") |
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input_ids = inputs["input_ids"].cuda() |
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pred = model.generate(input_ids, max_length=100, temperature=1.0, do_sample=True, use_cache=True) |
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print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True)) |
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""" |
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Sure, I can provide you with a three-day itinerary in Suzhou. Here's what we can do: |
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Day 1: |
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* Visit Suzhou Industrial Park, a major commercial and manufacturing district ... |
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""" |
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``` |
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## 📊 Performance |
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| Model | MMLU | ARC-c | HellaSeag | TruthfulQA | MT-Bench | |
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| :------------------------------------- | :---: | :---: | :-------: | :--------: | :------: | |
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| Sheared LLaMA-2.7B ShareGPT | 28.41 | 41.04 | 71.21 | 47.65 | 3.79 | |
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| Sheared LLaMA-2.7B Deita6K (Our Impl.) | 25.24 | 43.69 | 71.70 | 49.00 | 4.06 | |
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| LLaMA-MoE-v1-3.0B (2/16) | 23.61 | 43.43 | 72.28 | 44.24 | 4.15 | |
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| LLaMA-MoE-v1-3.5B (4/16) | 26.49 | 48.29 | 75.10 | 45.91 | 4.60 | |
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| LLaMA-MoE-v1-3.5B (2/8) | 25.53 | 45.99 | 74.95 | 44.39 | 4.72 | |
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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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``` |
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