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Model: THU-KEG/ADELIE-SFT-3B
Source: Original Platform

.gitattributes

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README.md

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+---
+license: llama2
+datasets:
+- ACE05
+- conll2003
+- conll2012_ontonotesv5
+- rams
+- tacred
+- fewrel
+- maven
+language:
+- en
+metrics:
+- f1
+pipeline_tag: text-generation
+tags:
+- text-generation-inference
+- Information Extraction
+- IE
+- Named Entity Recogniton
+- Event Extraction
+- Relation Extraction
+- LLaMA
+---
+
+# Model Card for ADELIE-SFT-3B
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+<p align="justify">
+We introduce <b>ADELIE</b> (<b>A</b>ligning large language mo<b>DEL</b>s on <b>I</b>nformation <b>E</b>xtraction), an aligned LLM that effectively solves various IE tasks, including closed IE, open IE, and on-demand IE. We first collect and construct a high-quality alignment corpus <font face="Verdana">IEInstruct</font> for IE. Then we train ADELIE<sub>SFT</sub> using instruction tuning on <font face="Verdana">IEInstruct</font>. We further train ADELIE<sub>SFT</sub> with direct preference optimization (DPO) objective, resulting in ADELIE<sub>DPO</sub>. Extensive experiments on various held-out IE datasets demonstrate that our models (ADELIE<sub>SFT</sub> and ADELIE<sub>DPO</sub>) achieve state-of-the-art (SoTA) performance among open-source models. We further explore the general capabilities of ADELIE, and experimental results reveal that their general capabilities do not exhibit a noticeable decline.
+
+- 📖 Paper: [ADELIE: Aligning Large Language Models on Information Extraction](https://arxiv.org/abs/2405.05008)
+</p>
+- 🐧 Github: [THU/ADELIE](https://github.com/THU-KEG/ADELIE/tree/main)
+
+
+# Model Performance
+
+The table below presents the average F1 scores (%) of the ADELIE model across closed IE, open IE, and on-demand IE tasks, as well as its overall performance (%) on general benchmarks. For dataset details, please refer to the paper.
+
+| Model           | Closed IE | Open IE | On-demand IE | General Average Score |
+|-----------------|-----------|---------|--------------|-----------------------|
+| Llama2 7B       | 5.7       | 5.6     | 22.4         | 52.2                  |
+| ADELIE-SFT      | 42.6      | 46.9    | 60.4         | 53.5                  |
+| ADELIE-DPO      | **42.7**      | **47.6**    | **60.5**         | **53.8**                  |
+|-----------------|-----------|---------|--------------|-----------------------|
+| Llama3.2 3B     | 19.1      | 18.5    | 20.8         | 55.5                  |
+| ADELIE-SFT-3B   | **41.8**      | 47.6    | **60.8**         | **55.6**                  |
+| ADELIE-DPO-3B   | 39.2      | **47.8**    | 60.7         | **55.6**                  |
+|-----------------|-----------|---------|--------------|-----------------------|
+| Qwen2.5 1.5B    | 16.5      | 14.2    | 20.5         | 54.6                  |
+| ADELIE-SFT-1.5B | 37.7      | 44.6    | 58.9         | 55.0                  |
+| ADELIE-DPO-1.5B | **38.5**      | **45.6**    | **59.2**         | **55.1**                  |
+
+
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** Yunjia Qi, Hao Peng, Xiaozhi Wang, Bin Xu, Lei Hou, Juanzi Li
+- **Model type:** Text Generation
+- **Language(s) (NLP):** English
+- **License:** LLaMA2 License for the base model. 
+- **Finetuned from model [optional]:** LLaMA3.2-3B
+

config.json

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+{
+  "_name_or_path": "/data3/MODELS/Llama-3.2-3B",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": 128001,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 24,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 32.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.44.0",
+  "use_cache": true,
+  "vocab_size": 128257
+}

configuration.json

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+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": 128001,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.44.0"
+}

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special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
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+}