Instructions to use ICTNLP/Auto-RAG-Llama-3-8B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ICTNLP/Auto-RAG-Llama-3-8B-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ICTNLP/Auto-RAG-Llama-3-8B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("ICTNLP/Auto-RAG-Llama-3-8B-Instruct")
model = AutoModelForMultimodalLM.from_pretrained("ICTNLP/Auto-RAG-Llama-3-8B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ICTNLP/Auto-RAG-Llama-3-8B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ICTNLP/Auto-RAG-Llama-3-8B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ICTNLP/Auto-RAG-Llama-3-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ICTNLP/Auto-RAG-Llama-3-8B-Instruct

SGLang

How to use ICTNLP/Auto-RAG-Llama-3-8B-Instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ICTNLP/Auto-RAG-Llama-3-8B-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ICTNLP/Auto-RAG-Llama-3-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ICTNLP/Auto-RAG-Llama-3-8B-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ICTNLP/Auto-RAG-Llama-3-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ICTNLP/Auto-RAG-Llama-3-8B-Instruct with Docker Model Runner:
```
docker model run hf.co/ICTNLP/Auto-RAG-Llama-3-8B-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Auto-RAG: Autonomous Retrieval-Augmented Generation for Large Language models

Tian Yu, Shaolei Zhang, and Yang Feng*

Model Details

Discription: This is Auto-RAG model trained with synthesized iterative retrieval instruction data. Details can be found in our paper.
Developed by: ICTNLP Group. Authors: Tian Yu, Shaolei Zhang and Yang Feng.
Github Repository: https://github.com/ictnlp/Auto-RAG
Paper Link: https://arxiv.org/abs/2411.19443
Finetuned from model: Meta-Llama3-8B-Instruct

Uses

You can directly deploy the model using vllm, such as:

CUDA_VISIBLE_DEVICES=6,7 python -m vllm.entrypoints.openai.api_server \
    --model PATH_TO_MODEL\
    --gpu-memory-utilization 0.9 \
    -tp 2 \
    --max-model-len 8192\
    --port 8000\
    --host 0.0.0.0

Citation

@article{yu2024autorag,
      title={Auto-RAG: Autonomous Retrieval-Augmented Generation for Large Language Models}, 
      author={Tian Yu and Shaolei Zhang and Yang Feng},
      year={2024},
      eprint={2411.19443},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2411.19443}, 
}

Downloads last month: 24

Safetensors

Model size

8B params

Tensor type

F32

Model tree for ICTNLP/Auto-RAG-Llama-3-8B-Instruct

Quantizations

2 models

Paper for ICTNLP/Auto-RAG-Llama-3-8B-Instruct

Auto-RAG: Autonomous Retrieval-Augmented Generation for Large Language Models

Paper • 2411.19443 • Published Nov 29, 2024