初始化项目，由ModelHub XC社区提供模型

Model: USTC-KnowledgeComputingLab/Llama3-KALE-LM-Chem-1.5-8B
Source: Original Platform

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README.md

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+---
+license: llama3
+language:
+- en
+base_model:
+- meta-llama/Meta-Llama-3-8B-Instruct
+tags:
+- KALE-LM
+- science
+- chemistry
+pipeline_tag: text-generation
+---
+
+# Llama3-KALE-LM-Chem-1.5-8B
+
+## Introduction
+
+We are thrilled to present Llama3-KALE-LM-Chem-1.5-8B, a new version of our open-source KALE-LM for science, which specializes in chemistry.
+
+We have trained our model with a larger amount of data.
+
+## Benchmarks
+
+### Open Benchmarks
+| Models | ChemBench | MMLU | MMLU-Chem | SciQ | IE(Acc) | IE(LS) |
+| ---- | ---- | ---- | ---- | ---- | ---- | ---- |
+| GPT-3.5 | 47.15 | 69.75 | 53.32 | 89.6 | 52.98 | 68.28 |
+| GPT-4 | 53.72 | 78.67 | 63.70 | 94.10 | 54.20 | 69.74 |
+| Llama3-8B-Instruct | 46.02 | 68.3 | 51.10 | 93.30 | 45.83 | 61.22 |
+| LlaSMol | 28.47 | 54.47 | 33.24 | 72.30 | 2.16 | 3.23 |
+| ChemDFM | 44.44 | 58.11 | 45.60 | 86.70 | 7.61 | 11.49 |
+| ChemLLM-7B-Chat | 34.16 | 61.79 | 48.39 | 94.00 | 29.66 | 39.17 |
+| ChemLLM-7B-Chat-1.5-SFT | 42.75 | 63.56 | 49.63 | **95.10** | 14.96 | 19.61 |
+| **Llama3-KALE-LM-Chem-1.5-8B** | **57.01** | 68.06 | **54.83** | 91.60 | **71.70** | **81.98** |
+
+#### ChemBench Details (Evaluated By OpenCompass)
+
+| Models | NC | PP | M2C | C2M | PP | RS | YP | TP | SP | Average |
+| ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ |
+| GPT-3.5 | 46.93 | 56.98 | 85.28 | 38.25 | 43.67 | 42.33 | 30.33 | 42.57 | 38 | 47.15 |
+| GPT-4 | 54.82 | 65.02 | 92.64 | 52.88 | 62.67 | 52.67 | 42.33 | 24.75 | 35.67 | 53.72 |
+| Llama3-8B-Instruct | 51.31 | 27.79 | 90.30 | 40.88 | 34.00 | 30.00 | 45.33 | 60.89 | 33.67 | 46.02 |
+| LlaSMol | 27.78 | 29.34 | 31.44 | 23.38 | 25.67 | 24.00 | 37.33 | 34.65 | 22.67 | 28.47 |
+| ChemDFM | 36.92 | 55.57 | 83.95 | 42.00 | 40.00 | 37.33 | 39.00 | 33.17 | 32.00 | 44.44 |
+| ChemLLM-7B-Chat | 41.05 | 29.76 | 85.28 | 26.12 | 26.00 | 24.00 | 20.00 | 24.26 | 31.00 | 34.16 |
+| 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 |
+| Llama3-KALE-LM-Chem-1.5-8B | 61.33 | 43.44 | 90.30 | 53.62 | 72.67 | 53.67 | 46.00 | 47.03 | 45.00 | 57.01 |
+
+## Cite This Work
+
+```
+@article{dai2024kale,
+  title={KALE-LM: Unleash The Power Of AI For Science Via Knowledge And Logic Enhanced Large Model},
+  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},
+  journal={arXiv preprint arXiv:2409.18695},
+  year={2024}
+}
+```

config.json

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+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": 128001,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.42.3",
+  "use_cache": false,
+  "vocab_size": 128256
+}

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+  "eos_token_id": [
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+  "transformers_version": "4.42.3"
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