readme

README.md

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 license: other
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+# LLaMA-30B-toolbench
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+LLaMA-30B-toolbench is a 30 billion parameter model used for api based action generation. It is instruction tuned from [LLaMA-30B](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) on api based action generation datasets.
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+- **Developed by:** [SambaNova Systems](https://sambanova.ai/)
+- **Model type:** Language Model
+- **Language(s):** English
+- **License:** Same as LLaMA model https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md
+
+- **Finetuned from model:** [LLaMA-30B](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md)
+
+### Basic Information
+
+<!-- Provide the basic links for the model. -->
+- **Paper**: [Link]
+- **Github**: [Link]
+
+
+## Uses
+<details>
+<summary>Click to expand</summary>
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+This model is intended for research use.
+
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+
+LLaMA-30B-toolbench should NOT be used for purpose other than API based action generation.
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users should be made aware of the risks, biases, limitations, and restrictions of the model. 
+
+</details>
+
+
+---
+## How to Get Started with the Model
+
+<details>
+<summary>Click to expand</summary>
+
+
+### Suggested Inference Parameters
+- do_sample: False
+
+### Suggested Prompts To Try in GPU Tutorial
+```
+Input text: 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
+```
+
+```
+Input text: 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
+```
+
+</details>
+
+---
+
+## Training Details
+
+<details>
+<summary>Click to expand</summary>
+
+### Training Data
+
+<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+The training data is curated for the 8 tasks in ToolBench. See Appendix A of the [paper](dummy link) 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](https://drive.google.com/file/d/1lUatLGnSVhfy1uVIPEQ7qCoLtnCIXi2O/view?usp=sharing) to the training data.
+
+
+### Training Procedure 
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+We trained LLaMA-30B-toolbench on 4 80GB A100 gpu's. We started from [LLaMA-30B](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md). We finetuned it on XXX dataset. 
+All of the code used to prepare the datasets and the scripts to run training and inference are open-sourced and freely available at [githublink here](dummy link)
+
+
+### Prompting Style Used For Training
+```
+
+```
+
+### 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
+
+
+
+</details>
+
+
+
+## Acknowledgment
+
+
+## Cite LLaMA-30B-toolbench
+```
+TBD
+```