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https://github.com/tangqiaoyu/ToolAlpaca

ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases
https://github.com/tangqiaoyu/ToolAlpaca

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ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases

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# ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases



[![arXiv](https://img.shields.io/badge/arXiv-2306.05301-.svg?style=flat-square)](https://arxiv.org/abs/2306.05301)

[![](https://img.shields.io/badge/huggingface-ToolAlpaca_7B-blue)](https://huggingface.co/TangQiaoYu/ToolAlpaca-7B)

[![](https://img.shields.io/badge/huggingface-ToolAlpaca_13B-blue)](https://huggingface.co/TangQiaoYu/ToolAlpaca-13B)






`ToolAlpaca` is a framework designed for learning generalized tool-use abilities in compact language models with minimal human supervision. It addresses the challenge of tool learning by generating a tool-use corpus via a multi-agent simulation environment, providing 3.9k tool-use instances from more than 400 tools. 






## Data



Dataset list:



- train_data.json: training data with 400+ APIs

- eval_simulated.json: evaluation data with 10 simulated APIs

- eval_real.json: evaluation data with 11 real APIs, some APIs require authentication.



Data format:



```json

{

  "Name": "name, from public-apis",

  "Description": "description, from public-apis",

  "Category": "category, from public-apis",

  "Introduction": "introduction, generated by LLM",

  "Functions": "NLDocumentation in paper v1, generated by LLM",

  "Documentation": "str(json), OpenAPI Specification documentation, generated by LLM",

  "NLDocumentation": "natural language documentation, similar to Functions, converted from Documentation",

  "Function_Description": "each functions description in NLDocumentation",

  "Function_Projection": "function to HTTP request method",

  "Instructions": "instructions, generated by LLM",

  "Instances": [

    {

      "input": "use's init instruction, from use agent",

      "output": "final output, from assistant agent",

      "Final Thought": "the final thought before output, from assistant agent",

      "intermediate_steps": [

        [

          [

            "action, from assistant agent",

            "action input, str(json), from assistant agent",

            "thought + action + action input, assistant agent's output"

          ]

          "bbservation, from [user agent, type check python code, tool executor agent]"

        ]

      ]

    }

  ]

}

```



## Dataset Generation

- Clone this repository and install packages

```bash

git clone [email protected]:tangqiaoyu/ToolAlpaca.git

cd ToolAlpaca

pip install -r requirements.txt

```



- download public-api data

```bash

python tool_maker/preprocess_public_apis.py -api data/public_apis.json

```



- toolset construction

```bash

export PYTHONPATH=$PYTHONPAT:$(pwd)

export OPENAI_API_KEY=""



python tool_maker/get_elements.py -api data/public_apis.json -out ./data



python tool_maker/natural_language_documentation.py -api ./data/api_data.json

```



- tool-use instances generation

```bash

python instance_generation/instruction.py -api ./data/api_data.json -out ./data



python instance_generation/simulator.py -api ./data/api_data.json



python instance_generation/generation.py -api ./data/api_data.json -out ./data --use_cache

```



## Train

To train Toolapaca, we need to create a prompt to organize the dataset in a format that the standard SFT training code can read, similar to what is done in `build_dataset.py`. Afterward, we can proceed with training using the standard SFT method, only optimizing the loss on `thought`, `action`, and `action input`.



```bash

deepspeed --num_gpus=2 --master_port=12345 train.py \

    --deepspeed ${deepspeed config path} \

    --model_name_or_path ${path to base model like vicuna-7b}  \

    --data_path ${data path} \

    --bf16 True \

    --output_dir outputs/vicuna-7b-toolalpaca/ \

    --num_train_epochs 3 \

    --per_device_train_batch_size 32 \

    --per_device_eval_batch_size 1 \

    --gradient_accumulation_steps 2 \

    --evaluation_strategy "no" \

    --save_strategy "epoch" \

    --save_total_limit 10 \

    --learning_rate 2e-5 \

    --weight_decay 0. \

    --warmup_ratio 0.03 \

    --lr_scheduler_type "cosine" \

    --logging_steps 1 \

    --tf32 True \

    --model_max_length 2048 \

    --gradient_checkpointing True \

    --lazy_preprocess True

```



You can Find our models on huggingface hub: [ToolAlpaca-7B](https://huggingface.co/TangQiaoYu/ToolAlpaca-7B), [ToolAlpaca-13B](https://huggingface.co/TangQiaoYu/ToolAlpaca-13B).



## Evaluation

- for simulated APIs:

```bash

# start the api simulator

python instance_generation/simulator.py -api ./data/eval_simulated.json



# get LLM outputs

python instance_generation/generation.py \

  -api ./data/eval_simulated.json \

  -out ./eval \

  -llm TangQiaoYu/ToolAlpaca-13B \

  --agent_prompt test_v1 \

  --use_cache



# evaluation with LLM like GPT-4

python evaluation.py -api ${api_data_path} -out ./eval

```



- for real APIs:

You should register the websites and get the API_KEYs.



```bash

python instance_generation/generation.py \

  -api ./data/eval_real.json \

  -out ./data \

  -llm TangQiaoYu/ToolAlpaca-13B \

  --agent_prompt test_v1 \

  --real



python evaluation.py -api ${api_data_path} -out ./eval

```



## Citation



If you find our work helpful, please cite as



```bibtex

@misc{tang2023toolalpaca,

      title={ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases}, 

      author={Qiaoyu Tang and Ziliang Deng and Hongyu Lin and Xianpei Han and Qiao Liang and Le Sun},

      year={2023},

      eprint={2306.05301},

      archivePrefix={arXiv},

      primaryClass={cs.CL}

}

```