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FreedomIntelligence/
RAG-Instruct-Llama3-3B

Name: RAG-Instruct-Llama3-3B
Rating: 2.5 (65 reviews)

safetensors

text-generation

FreedomIntelligence/RAG-Instruct

2501.00353

meta-llama/Llama-3.2-3B

finetune

apache-2.0

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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, covering a wide range of RAG scenarios and tasks.

Our RAG-Instruct-Llama3-3B is trained on 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

Usage

You can deploy it with tools like vllm or 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}, 
}