初始化项目，由ModelHub XC社区提供模型

Model: voidism/SelfCite-8B-CC-SFT
Source: Original Platform

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README.md

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+---
+license: llama3.1
+library_name: transformers
+pipeline_tag: text-generation
+base_model: meta-llama/Llama-3.1-8B-Instruct
+tags:
+- selfcite
+- contextcite
+- context-attribution
+- llama
+- safetensors
+- arxiv:2502.09604
+- arxiv:2409.02897
+---
+
+# SelfCite-8B-CC-SFT
+
+Paper: [SelfCite: Self-Supervised Alignment for Context Attribution in Large Language Models](https://arxiv.org/abs/2502.09604)  
+Authors: [Yung-Sung Chuang](https://people.csail.mit.edu/yungsung/)†, [Benjamin Cohen-Wang](https://bencw99.github.io/)†, [Shannon Zejiang Shen](https://www.szj.io/)†, [Zhaofeng Wu](https://zhaofengwu.github.io/)†, [Hu Xu](https://howardhsu.github.io/)‡, [Xi Victoria Lin](https://victorialin.org/)‡, [James Glass](https://people.csail.mit.edu/jrg/)†, [Shang-Wen Li](https://swdanielli.github.io/)‡, [Wen-tau Yih](https://scottyih.org/)‡  
+† Massachusetts Institute of Technology, ‡ Meta AI
+
+![SelfCite overview](https://github.com/facebookresearch/SelfCite/raw/main/SelfCite.png)
+
+This is the intermediate **ContextCite supervised fine-tuning (CC-SFT)** checkpoint used in the fully self-supervised setting of the SelfCite paper. It is initialized from [Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) and trained on SFT data generated with [ContextCite](https://github.com/MadryLab/context-cite) using 256 ContextCite calls.
+
+This checkpoint is the model **before SimPO preference optimization**. For the resulting SelfCite model after SimPO, use [voidism/SelfCite-8B-from-CC](https://huggingface.co/voidism/SelfCite-8B-from-CC).
+
+Please refer to the [SelfCite repository](https://github.com/facebookresearch/SelfCite/tree/main/training) for training and usage details.
+
+## Training details
+
+- Base model: `meta-llama/Llama-3.1-8B-Instruct`
+- Training examples: 11,185
+- Learning rate: `7e-6`
+- Epochs: 1
+- Per-device batch size: 1
+- Number of devices: 8
+- Effective batch size: 8
+- Scheduler: cosine with a 0.1 warmup ratio
+- Precision: bfloat16
+- Transformers: 4.44.2
+- PyTorch: 2.4.1+cu121
+
+## Usage
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "voidism/SelfCite-8B-CC-SFT"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+```
+
+## Citation
+
+Please cite SelfCite and LongCite if they are helpful to your work.
+
+```bibtex
+@inproceedings{chuang2025selfcite,
+  title={SelfCite: Self-Supervised Alignment for Context Attribution in Large Language Models},
+  author={Yung-Sung Chuang and Benjamin Cohen-Wang and Shannon Zejiang Shen and Zhaofeng Wu and Hu Xu and Xi Victoria Lin and James Glass and Shang-Wen Li and Wen-tau Yih},
+  journal={arXiv preprint arXiv:2502.09604},
+  year={2025}
+}
+
+@article{zhang2024longcite,
+  title={LongCite: Enabling LLMs to Generate Fine-grained Citations in Long-context QA},
+  author={Jiajie Zhang and Yushi Bai and Xin Lv and Wanjun Gu and Danqing Liu and Minhao Zou and Shulin Cao and Lei Hou and Yuxiao Dong and Ling Feng and Juanzi Li},
+  journal={arXiv preprint arXiv:2409.02897},
+  year={2024}
+}
+```

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+  "architectures": [
+    "LlamaForCausalLM"
+  ],
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+  "mlp_bias": false,
+  "model_type": "llama",
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+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
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+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.44.2",
+  "use_cache": true,
+  "vocab_size": 128256
+}

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+  "top_p": 0.9,
+  "transformers_version": "4.44.2"
+}

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