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Model: FreedomIntelligence/RAG-Instruct-Llama3-3B
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+datasets:
+- FreedomIntelligence/RAG-Instruct
+language:
+- en
+metrics:
+- accuracy
+base_model:
+- meta-llama/Llama-3.2-3B
+pipeline_tag: text-generation
+---
+
+## Introduction
+
+RAG-Instructis a method for generating diverse and high-quality RAG instruction data. It synthesizes instruction datasets based on any source corpus, leveraging the following approaches:
+
+- **Five RAG paradigms**, which represent diverse query-document relationships to enhance model generalization across tasks.
+- **Instruction simulation**, which enriches instruction diversity and quality by utilizing the strengths of existing instruction datasets.
+
+Using this approach, we constructed [RAG-Instruct](https://huggingface.co/datasets/FreedomIntelligence/RAG-Instruct), covering a wide range of RAG scenarios and tasks. 
+
+Our RAG-Instruct-Llama3-3B is trained on [RAG-Instruct](https://huggingface.co/datasets/FreedomIntelligence/RAG-Instruct) data, which significantly enhances the RAG ability of LLMs, demonstrating remarkable improvements in RAG performance across various tasks.
+
+| Model                          | WQA (acc) | PQA (acc) | TQA (acc) | OBQA (EM) | Pub (EM) | ARC (EM) | 2WIKI (acc) | HotP (acc) | MSQ (acc) | CFQA (EM) | PubMed (EM) |
+|--------------------------------|-----------|-----------|-----------|-----------|----------|----------|-------------|------------|-----------|-----------|-------------|
+| Llama3.2-3B                    | 58.7 | 61.8 | 69.7 |  77.0 | 55.0 | 66.8 | 55.6 | 40.2 | 13.2 | 46.8 | 70.3 |
+| Llama3.2-3B + **RAG-Instruct**     | 65.3                      | 64.0                | 77.0               |  81.2                           | 66.4                    | 73.0                    | 72.9                 | 52.7           | 25.0            | 50.3                     | 72.6                    | 
+
+# <span>Usage</span>
+You can deploy it with tools like [vllm](https://github.com/vllm-project/vllm) or [Sglang](https://github.com/sgl-project/sglang),  or perform direct inference:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load the model and tokenizer
+model = AutoModelForCausalLM.from_pretrained("FreedomIntelligence/RAG-Instruct-Llama3-3B",torch_dtype="auto",device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("FreedomIntelligence/RAG-Instruct-Llama3-3B")
+
+# Example input
+input_text = """### Paragraph:
+[1] structure is at risk from new development...
+[2] as Customs and Excise stores...
+[3] Powis Street is partly underway...
+...
+
+### Instruction:
+Which organization is currently using a building in Woolwich that holds historical importance?
+"""
+
+# Tokenize and prepare input
+messages = [{"role": "user", "content": input_text}]
+inputs = tokenizer(tokenizer.apply_chat_template(messages, tokenize=False,add_generation_prompt=True), return_tensors="pt").to(model.device)
+
+# Generate output
+outputs = model.generate(**inputs, max_new_tokens=2048)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Citation
+```
+@misc{liu2024raginstructboostingllmsdiverse,
+      title={RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented Instructions}, 
+      author={Wanlong Liu and Junying Chen and Ke Ji and Li Zhou and Wenyu Chen and Benyou Wang},
+      year={2024},
+      eprint={2501.00353},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2501.00353}, 
+}
+```

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