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--- |
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license: llama3 |
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base_model: meta-llama/Meta-Llama-3-8B-Instruct |
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language: |
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- en |
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tags: |
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- KALE-LM |
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- science |
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- chemistry |
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pipeline_tag: text-generation |
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--- |
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# Llama3-KALE-LM-Chem-8B |
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## Introduction |
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We are thrilled to present Llama3-KALE-LM-Chem 8B, our first open-source KALE-LM, which specializes in chemistry. |
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## Training Details |
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We have continually pre-trained the model with a large amount of data and post-trained it through supervised fine-tuning. |
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## Benchmarks |
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### Open Benchmarks |
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| Models | ChemBench | MMLU | MMLU-Chem | SciQ | IE(Acc) | IE(LS) | |
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| ---- | ---- | ---- | ---- | ---- | ---- | ---- | |
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| GPT-3.5 | 47.15 | 69.75 | 53.32 | 89.6 | 52.98 | 68.28 | |
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| GPT-4 | 53.72 | 78.67 | 63.70 | 94.10 | 54.20 | 69.74 | |
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| Llama3-8B-Instruct | 46.02 | 68.3 | 51.10 | 93.30 | 45.83 | 61.22 | |
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| LlaSMol | 28.47 | 54.47 | 33.24 | 72.30 | 2.16 | 3.23 | |
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| ChemDFM | 44.44 | 58.11 | 45.60 | 86.70 | 7.61 | 11.49 | |
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| ChemLLM-7B-Chat | 34.16 | 61.79 | 48.39 | 94.00 | 29.66 | 39.17 | |
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| ChemLLM-7B-Chat-1.5-SFT | 42.75 | 63.56 | 49.63 | **95.10** | 14.96 | 19.61 | |
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| **Llama3-KALE-LM-Chem-8B** | **52.40** | **68.74** | **53.83** | 91.50 | **67.50** | **78.37** | |
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#### ChemBench Details (Evaluated By OpenCompass) |
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| Models | NC | PP | M2C | C2M | PP | RS | YP | TP | SP | Average | |
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| ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | |
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| GPT-3.5 | 46.93 | 56.98 | 85.28 | 38.25 | 43.67 | 42.33 | 30.33 | 42.57 | 38 | 47.15 | |
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| GPT-4 | 54.82 | 65.02 | 92.64 | 52.88 | 62.67 | 52.67 | 42.33 | 24.75 | 35.67 | 53.72 | |
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| Llama3-8B-Instruct | 51.31 | 27.79 | 90.30 | 40.88 | 34.00 | 30.00 | 45.33 | 60.89 | 33.67 | 46.02 | |
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| LlaSMol | 27.78 | 29.34 | 31.44 | 23.38 | 25.67 | 24.00 | 37.33 | 34.65 | 22.67 | 28.47 | |
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| ChemDFM | 36.92 | 55.57 | 83.95 | 42.00 | 40.00 | 37.33 | 39.00 | 33.17 | 32.00 | 44.44 | |
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| ChemLLM-7B-Chat | 41.05 | 29.76 | 85.28 | 26.12 | 26.00 | 24.00 | 20.00 | 24.26 | 31.00 | 34.16 | |
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| ChemLLM-7B-Chat-1.5-SFT | 50.06 | 49.51 | 85.28 | 38.75 | 38.00 | 26.67 | 28.33 | 31.68 | 33.67 | 42.44 | |
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| Llama3-KALE-LM-Chem-8B | 63.58 | 58.39 | 92.98 | 44.50 | 48.67 | 38.33 | 46.33 | 44.55 | 34.33 | 52.41 | |
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## Quick Start |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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device = "cuda" # the device to load the model onto |
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model = AutoModelForCausalLM.from_pretrained( |
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"USTC-KnowledgeComputingLab/Llama3-KALE-LM-Chem-8B", |
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torch_dtype="auto", |
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device_map="auto" |
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) |
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tokenizer = AutoTokenizer.from_pretrained("USTC-KnowledgeComputingLab/Llama3-KALE-LM-Chem-8B") |
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prompt = "Give me a short introduction to large language model." |
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messages = [ |
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{"role": "system", "content": "You are a helpful assistant."}, |
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{"role": "user", "content": prompt} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(device) |
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generated_ids = model.generate( |
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model_inputs.input_ids, |
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max_new_tokens=2048 |
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) |
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generated_ids = [ |
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
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] |
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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``` |
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## Cite This Work |
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``` |
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@article{dai2024kale, |
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title={KALE-LM: Unleash The Power Of AI For Science Via Knowledge And Logic Enhanced Large Model}, |
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author={Dai, Weichen and Chen, Yezeng and Dai, Zijie and Huang, Zhijie and Liu, Yubo and Pan, Yixuan and Song, Baiyang and Zhong, Chengli and Li, Xinhe and Wang, Zeyu and others}, |
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journal={arXiv preprint arXiv:2409.18695}, |
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year={2024} |
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} |
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``` |
