# Ziya

In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on Ziya models. For illustration purposes, we utilize the [IDEA-CCNL/Ziya-Coding-34B-v1.0](https://huggingface.co/IDEA-CCNL/Ziya-Coding-34B-v1.0) as a reference Ziya model.

> **Note**: If you want to download the Hugging Face *Transformers* model, please refer to [here](https://huggingface.co/docs/hub/models-downloading#using-git).
>
> IPEX-LLM optimizes the *Transformers* model in INT4 precision at runtime, and thus no explicit conversion is needed.

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

## Example: Predict Tokens using `generate()` API
In the example [generate.py](./generate.py), we show a basic use case for a Ziya model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
### 1. Install
We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://docs.conda.io/en/latest/miniconda.html#).

After installing conda, create a Python environment for IPEX-LLM:

On Linux:

```bash
conda create -n llm python=3.11 # recommend to use Python 3.11
conda activate llm

# install the latest ipex-llm nightly build with 'all' option
pip install --pre --upgrade ipex-llm[all] --extra-index-url https://download.pytorch.org/whl/cpu
pip install einops  # additional package required for Ziya to conduct generation
```

On Windows:

```cmd
conda create -n llm python=3.11
conda activate llm

pip install --pre --upgrade ipex-llm[all]
pip install einops
```

### 2. Run
After setting up the Python environment, you could run the example by following steps.

> **Note**: When loading the model in 4-bit, IPEX-LLM converts linear layers in the model into INT4 format. In theory, a *X*B model saved in 16-bit will requires approximately 2*X* GB of memory for loading, and ~0.5*X* GB memory for further inference.
>
> Please select the appropriate size of the Ziya model based on the capabilities of your machine.

#### 2.1 Client
On client Windows machines, it is recommended to run directly with full utilization of all cores:
```cmd
python ./generate.py --prompt 'def quick_sort(arr):\n'
```
More information about arguments can be found in [Arguments Info](#23-arguments-info) section. The expected output can be found in [Sample Output](#24-sample-output) section.

#### 2.2 Server
For optimal performance on server, it is recommended to set several environment variables (refer to [here](../README.md#best-known-configuration-on-linux) for more information), and run the example with all the physical cores of a single socket.

E.g. on Linux,
```bash
# set IPEX-LLM env variables
source ipex-llm-init

# e.g. for a server with 48 cores per socket
export OMP_NUM_THREADS=48
numactl -C 0-47 -m 0 python ./generate.py --prompt 'def quick_sort(arr):\n'
```
More information about arguments can be found in [Arguments Info](#23-arguments-info) section. The expected output can be found in [Sample Output](#24-sample-output) section.

#### 2.3 Arguments Info
In the example, several arguments can be passed to satisfy your requirements:

- `--repo-id-or-model-path`: str, argument defining the huggingface repo id for the Ziya model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'IDEA-CCNL/Ziya-Coding-34B-v1.0'`.
- `--prompt`: str, argument defining the prompt to be inferred (with integrated prompt format for chat). It is default to be `def quick_sort(arr):\n`.
- `--n-predict`: int, argument defining the max number of tokens to predict. It is default to be `128`.

#### 2.4 Sample Output
#### [IDEA-CCNL/Ziya-Coding-34B-v1.0](https://huggingface.co/IDEA-CCNL/Ziya-Coding-34B-v1.0)
```log
Inference time: xxxx s
-------------------- Prompt --------------------
<human>: 
def quick_sort(arr):\n
<bot>: 

-------------------- Output --------------------
<s> <human>: 
def quick_sort(arr):\n
<bot>: 
def partition(arr, low, high):

    i = (low-1)
    pivot = arr[high]
    for j in range(low, high):
        if arr[j] <= pivot:
            arr[i], arr[j] = arr[j], arr[i]
            i = i+1
    arr[i], arr[high] = arr[high], arr[i]
    return i

def quick_sort(arr, low, high):
    if low < high:
        pi = partition(arr, low,
```