Instructions to use ai2lumos/lumos_web_agent_plan_iterative-13B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ai2lumos/lumos_web_agent_plan_iterative-13B with Transformers:

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

pipe = pipeline("text-generation", model="ai2lumos/lumos_web_agent_plan_iterative-13B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ai2lumos/lumos_web_agent_plan_iterative-13B")
model = AutoModelForCausalLM.from_pretrained("ai2lumos/lumos_web_agent_plan_iterative-13B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ai2lumos/lumos_web_agent_plan_iterative-13B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ai2lumos/lumos_web_agent_plan_iterative-13B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ai2lumos/lumos_web_agent_plan_iterative-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ai2lumos/lumos_web_agent_plan_iterative-13B

SGLang

How to use ai2lumos/lumos_web_agent_plan_iterative-13B 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 "ai2lumos/lumos_web_agent_plan_iterative-13B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ai2lumos/lumos_web_agent_plan_iterative-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "ai2lumos/lumos_web_agent_plan_iterative-13B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ai2lumos/lumos_web_agent_plan_iterative-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ai2lumos/lumos_web_agent_plan_iterative-13B with Docker Model Runner:
```
docker model run hf.co/ai2lumos/lumos_web_agent_plan_iterative-13B
```

🪄 Agent Lumos: Unified and Modular Training for Open-Source Language Agents

🌐[Website] 📝[Paper] 🤗[Data] 🤗[Model] 🤗[Demo]

We introduce 🪄Lumos, Language Agents with Unified Formats, Modular Design, and Open-Source LLMs. Lumos unifies a suite of complex interactive tasks and achieves competitive performance with GPT-4/3.5-based and larger open-source agents.

Lumos has following features:

🧩 Modular Architecture:
- 🧩 Lumos consists of planning, grounding, and execution modules built based on LLAMA-2-7B/13B and off-the-shelf APIs.
- 🤗 Lumos utilizes a unified data format that encompasses multiple task types, thereby enabling the developed agent framework to conveniently support a range of interactive tasks.
🌍 Diverse Training Data:
- 🌍 Lumos is trained with ~56K diverse high-quality subgoal/action annotations from ground-truth reasoning steps in existing benchmarks with GPT-4.
- ⚒️ Lumos data can be instrumental for future research in developing open-source agents for complex interactive tasks.
🚀 Competitive Performance:
- 🚀 Lumos is comparable or even beats GPT-series agents on web/complex QA tasks Mind2Web and HotpotQA, and larger open agents on math and multimodal tasks.
- 🚀 Lumos exceeds contemporaneous agents that have been fine-tuned with in-domain HotpotQA, Mind2Web and ScienceQA annotations, such as FiReAct, AgentLM, and AutoAct.
- 🚀 Lumos performs better than open agent baseline formulations including chain-of-thoughts and integrated training.
- 🚀 Lumos surpasses larger open LLM agents and domain-specific agents on unseen tasks, WebShop and InterCode_SQL.

Model Overview

lumos_web_agent_plan_iterative-13B is a planning module checkpoint finetuned on web agent task in Lumos-Iterative (Lumos-I) formulation.

The training annotation is shown below:

Training Data	Number
`lumos_web_agent_plan_iterative-13B`	1009

Citation

If you find this work is relevant with your research, please feel free to cite our work!

@article{yin2023lumos,
  title={Agent Lumos: Unified and Modular Training for Open-Source Language Agents},
  author={Yin, Da and Brahman, Faeze and Ravichander, Abhilasha and Chandu, Khyathi and Chang, Kai-Wei and Choi, Yejin and Lin, Bill Yuchen},
  journal={arXiv preprint arXiv:2311.05657},
  year={2023}
}

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Dataset used to train ai2lumos/lumos_web_agent_plan_iterative-13B

Paper for ai2lumos/lumos_web_agent_plan_iterative-13B

Lumos: Learning Agents with Unified Data, Modular Design, and Open-Source LLMs

Paper • 2311.05657 • Published Nov 9, 2023 • 30