---
license: bsd-3-clause
---
# codgen-16B-action

<!-- Provide a quick summary of what the model is/does. -->

codgen-16B-action is a 16 billion parameter model used for api based action generation. It is instruction tuned from [codegen-16B-mono](https://huggingface.co/Salesforce/codegen-16B-mono) 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:** 
- **Finetuned from model:** [codegen-16B-mono](https://huggingface.co/Salesforce/codegen-16B-mono)

### Basic Information

<!-- Provide the basic links for the model. -->
- **Paper**: [Link]
- **Github**: [Link]

### Licensing

TBD

## 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 commercial and research use.


### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->


codgen-16B-action 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, which are listed down at the bottom of the page. 

</details>


---
## How to Get Started with the Model

<details>
<summary>Click to expand</summary>

### Loading in model with Huggingface

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/codegen-16b-action")
model = AutoModelForCausalLM.from_pretrained("sambanovasystems/codegen-16b-action", device_map="auto", torch_dtype="auto")
```

### Suggested Inference Parameters
- do_sample: False

### Suggested Prompts To Try in GPU Tutorial
```
Input text: Fenglu, can you add some?
```

```
Input text: Fenglu, can you add some?
```

```
Input text: 十七岁的风是什么颜色的?
```


</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. -->

- [Fenglu to add](https://huggingface.co/datasets/laion/OIG)


### 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 codegen-16b-action on 4 80GB A100 gpu's. We started from [codegen-16B-mono](https://huggingface.co/Salesforce/codegen-16B-mono). 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

**Instruction-tuned Training on Dolly 2.0 and Oasst1**

- Hardware: SambaNova Reconfigurable Dataflow Unit (RDU)
- Optimizer: AdamW
- Grad accumulation: 1
- Epochs: 3
- Global Batch size: 128
- Batch tokens: 128 * 2048 = 262,144 tokens
- Learning Rate: 1e-5
- Learning Rate Scheduler: Cosine Schedule with Warmup
- Warmup Steps: 0
- End Learning Ratio: 0.1
- Weight decay: 0.1

</details>



## Acknowledgment


## Cite codegen-16b-action
```
@software{bloomchat,
  title = {{BLOOMChat: a New Open Multilingual Chat LLM}},
  author = {SambaNova Systems, Together Computer},
  url = {https://huggingface.co/sambanovasystems/BLOOMChat-176B-v1}
  month = {5},
  year = {2023},
  version = {1.0},
}
```