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sambanovasystems/
codegen-16B-mono-toolbench

Name: codegen-16B-mono-toolbench
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codgen-16B-mono-toolbench

codgen-16B-mono-toolbench is a 16 billion parameter model used for api based action generation. It is instruction tuned from codegen-16B-mono on api based action generation datasets.

Model Details

Model Description

Developed by: SambaNova Systems
Model type: Language Model
Language(s): English
License: bsd-3-clause
Finetuned from model: codegen-16B-mono

Basic Information

Paper: Link
Github: link

Uses

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Direct Use

This model is intended for commercial and research use.

Out-of-Scope Use

codgen-16B-mono-toolbench should NOT be used for purpose other than API based action generation.

How to Get Started with the Model

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Loading in model with Huggingface

python

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/codegen-16B-mono-toolbench")
model = AutoModelForCausalLM.from_pretrained("sambanovasystems/codegen-16B-mono-toolbench", device_map="auto", torch_dtype="auto")

Suggested Inference Parameters

do_sample: False

Example Prompts To Try in GPU Tutorial

Prompt 1:

I have the following set of API:\n\n# To set the maximum commute time in minute to your office location, assuming the office location is already defined\nAPI.set_max_commute_time(value: int)\n\n# To set the maximum home size in square feet\nAPI.set_max_square_feet(value: int)\n\n# To set the minimum home price in dollars\nAPI.set_min_price(value: int)\n\n# To set the number of garage(s)\nAPI.set_num_garages(value: int)\n\n# To set home types for search. For home buying, home_types choices are: \"House\", \"Townhouse\", \"Condo\", \"Land\", \"Multi-family\", \"Mobile\", \"Co-op\"; for home renting, home_types choices are: \"House\", \"Townhouse\", \"Condo\", \"Apartment\".\nAPI.select_home_type(home_types: List[str])\n\n# To set the number of balconies\nAPI.set_num_balconies(value: int)\n\n# Submit criterion to get search results. This function should be called after setting all the criterion.\nAPI.search()\n\n# To set the floor number\nAPI.set_floor_number(value: int)\n\n# To set the number of bedroom(s)\nAPI.set_num_beds(value: int)\n\n# To set the number of swimming pool(s)\nAPI.set_num_swimming_pools(value: int)\n\n# To set the maximum home price in dollars\nAPI.set_max_price(value: int)\n\n# To specify whether to search homes for buying or renting. 'value' can be chosen from ['buy', 'rent']. This function must be called after setting the location and before setting any other criteria.\nAPI.set_buy_or_rent(value: str)\n\n# To set the number of bathroom(s)\nAPI.set_num_baths(value: float)\n\n# To set the location for the search area. This function must be called before setting any criteria.\nAPI.set_location(value: string)\n\n# To set the minimum home size in square feet\nAPI.set_min_square_feet(value: int)\n\n-------------\n\nTask: Looking for homes to rent in Santa Clarita with a price range between $110000 and $1753000, a minimum of 1700 square feet, at least 2 balconies, and 3.5 bathrooms.\nAction:\n

Prompt 2:

I have the following set of API:\n\n# To set the location for hotel search, given a Loc object. This function must be called if booking type is 'hotels' or 'both'.\nAPI.set_hotel_location(Loc)\n\n# To set the number of hotel rooms to book.\nAPI.set_num_rooms(value)\n\n# To set the location for departure, given a Loc object. This function must be called if booking type is 'trip tickets' or 'both'.\nAPI.set_origin(Loc)\n\n# To select the transportation type from ['flight', 'train', 'bus', 'cruise']. This function must be called if booking type is 'trip tickets' or 'both'.\nAPI.select_transportation(transportation_type)\n\n# To set the return date of the trip, given a Date object. If booking type is 'both' and this function is not called explicitly, 'return_date' will be set to 'hotel_checkout_date' implicitly.\nAPI.set_return_date(Date)\n\n# To set the hotel check-in date, given a Date object. This function must be called if booking type is 'hotels' or 'both'.\nAPI.set_checkin_date(Date)\n\n# To define a date.\ndate = Date(month, day, year)\n\n# To set the departure date of the trip, given a Date object. This function must be called if booking type is 'trip tickets'. If booking type is 'both' and this function is not called explicitly, 'departure_date' will be set to 'hotel_checkin_date' implicitly.\nAPI.set_departure_date(Date)\n\n# To set the location for arrival, given a Loc object. This function must be called if booking type is 'trip tickets' or 'both'.\nAPI.set_destination(Loc)\n\n# To define a location of a given city 'City'.\nlocation = Loc('City')\n\n# To set maximum hotel room price.\nAPI.set_max_room_price(value)\n\n# To set minimum ticket price.\nAPI.set_min_ticket_price(value)\n\n# To select the booking type from ['hotels', 'trip tickets', 'both']. This function must be called before setting any criteria.\nAPI.select_booking_type(booking_type)\n\n# To set minimum hotel room price.\nAPI.set_min_room_price(value)\n\n# To set the number of child tickets to purchase.\nAPI.set_num_children(value)\n\n# To set the number of adult tickets to purchase.\nAPI.set_num_adults(value)\n\n# To select the hotel room type from ['King Bed', 'Queen Bed', 'Double', 'Luxury'].\nAPI.select_room_type(room_type)\n\n# To set maximum ticket price.\nAPI.set_max_ticket_price(value)\n\n# Submit criterion to get search results. This function should be called after setting all the criterion.\nAPI.search()\n\n# To set the hotel check-out date, given a Date object. This function must be called if booking type is 'hotels' or 'both'.\nAPI.set_checkout_date(Date)\n\n-------------\n\nTask: Looking to book 2 adult and 4 child tickets from Stockton to Baltimore by cruise, on 2023-07-29.\nAction:\n

Training Details

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Training Data

The training data is curated for the 8 tasks in ToolBench. See Appendix A of the paper for task details and Appendix C.1 for the training data curation details. In total, there are 9704 training samples, organized in all-shot format as described in Appendix C.2. Here is the download link to the training data.

Training Procedure

We trained codegen-16b-mono-toolbench on 4 80GB A100 gpu's. We started from codegen-16B-mono and finetuned it on the dataset mentioned above.

Hyperparameters

Hardware: A100 GPU
Optimizer: AdamW
Grad accumulation: 1
Epochs: 8
Global Batch size: 16
Batch tokens: 16 * 2048 = 32,768 tokens
Learning Rate: 1e-5
Learning Rate Scheduler: Fixed LR
Weight decay: 0.1

Acknowledgment

We would like to express our gratitude to the great work done in CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis

Cite codegen-16b-mono-toolbench

@misc{xu2023tool,
      title={On the Tool Manipulation Capability of Open-source Large Language Models}, 
      author={Qiantong Xu and Fenglu Hong and Bo Li and Changran Hu and Zhengyu Chen and Jian Zhang},
      year={2023},
      eprint={2305.16504},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}