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README.md
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---
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license:
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---
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license: bsd-3-clause
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# starcoder-toolbench
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<!-- Provide a quick summary of what the model is/does. -->
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starcoder-toolbench is a 15 billion parameter model used for api based action generation. It is instruction tuned from [starcoder](https://huggingface.co/bigcode/starcoder) on api based action generation datasets.
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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- **Developed by:** [SambaNova Systems](https://sambanova.ai/)
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- **Model type:** Language Model
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- **Language(s):** English
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- **License:**
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- **Finetuned from model:** [starcoder](https://huggingface.co/bigcode/starcoder)
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### Basic Information
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<!-- Provide the basic links for the model. -->
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- **Paper**: [Link]
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- **Github**: [Link]
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### Licensing
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TBD
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## Uses
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<details>
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<summary>Click to expand</summary>
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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This model is intended for commercial and research use.
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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starcoder-toolbench should NOT be used for purpose other than API based action generation.
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users should be made aware of the risks, biases, limitations, and restrictions of the model, which are listed down at the bottom of the page.
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</details>
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---
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## How to Get Started with the Model
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<details>
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<summary>Click to expand</summary>
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### Loading in model with Huggingface
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/starcoder-toolbench")
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model = AutoModelForCausalLM.from_pretrained("sambanovasystems/starcoder-toolbench", device_map="auto", torch_dtype="auto")
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```
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</details>
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---
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## Training Details
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<details>
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<summary>Click to expand</summary>
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### Training Data
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<!-- 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. -->
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- [Fenglu to add](https://huggingface.co/datasets/laion/OIG)
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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We trained starcoder-toolbench on 4 80GB A100 gpu's. We started from [starcoder](https://huggingface.co/bigcode/starcoder). We finetuned it on XXX dataset.
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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)
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### Prompting Style Used For Training
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```
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```
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### Hyperparameters
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- Hardware: A100 GPU
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- Optimizer: AdamW
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- Grad accumulation: 1
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- Epochs: 8
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- Global Batch size: 16
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- Batch tokens: 16 * 2048 = 32,768 tokens
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- Learning Rate: 1e-5
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- Learning Rate Scheduler: Fixed LR
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- Weight decay: 0.1
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</details>
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## Acknowledgment
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## Cite starcoder-toolbench
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```
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@software{bloomchat,
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title = {{BLOOMChat: a New Open Multilingual Chat LLM}},
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author = {SambaNova Systems, Together Computer},
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url = {https://huggingface.co/sambanovasystems/BLOOMChat-176B-v1}
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month = {5},
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year = {2023},
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version = {1.0},
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}
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```
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