# StarCoder
In this directory, you will find examples on how you could apply BigDL-LLM INT4 optimizations on StarCoder models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [bigcode/starcoder](https://huggingface.co/bigcode/starcoder) as a reference StarCoder model.

## 0. Requirements
To run these examples with BigDL-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to [here](../../../README.md#requirements) for more information.

## Example: Predict Tokens using `generate()` API
In the example [generate.py](./generate.py), we show a basic use case for an StarCoder model to predict the next N tokens using `generate()` API, with BigDL-LLM INT4 optimizations on Intel GPUs.
### 1. Install
We suggest using conda to manage environment:
```bash
conda create -n llm python=3.9
conda activate llm
# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
```

### 2. Configures OneAPI environment variables
```bash
source /opt/intel/oneapi/setvars.sh
```

### 3. Run

For optimal performance on Arc, it is recommended to set several environment variables.

```bash
export USE_XETLA=OFF
export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
```

```
python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT
```

Arguments info:
- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the StarCoder model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'bigcode/starcoder'`.
- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'def print_hello_world():'`.
- `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`.

#### Sample Output
#### [bigcode/starcoder](https://huggingface.co/bigcode/starcoder)
```log
Loading checkpoint shards: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [02:07<00:00, 18.23s/it]
The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.
The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.
Inference time: xxxx s
-------------------- Prompt --------------------
def print_hello_world():
-------------------- Output --------------------
def print_hello_world():
    print("Hello World!")


def print_hello_name(name):
    print(f"Hello {name}!")


def print_
```