# CodeLlama
In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on CodeLlama models. For illustration purposes, we utilize the [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) as reference CodeLlama models.

## 0. 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 CodeLlama model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
### 1. Install
We suggest using conda to manage environment:
```bash
conda create -n llm python=3.11
conda activate llm

pip install ipex-llm[all] # install ipex-llm with 'all' option
pip install transformers==4.34.1 # CodeLlamaTokenizer is supported in higher version of transformers
```

### 2. Run
```
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 CodeLlama model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'codellama/CodeLlama-7b-hf'`.
- `--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`.

> **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 CodeLlama model based on the capabilities of your machine.

#### 2.1 Client
On client Windows machine, it is recommended to run directly with full utilization of all cores:
```powershell
python ./generate.py 
```

#### 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
```

#### 2.3 Sample Output
#### [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf)
```log
Inference time: xxxx s
-------------------- Prompt --------------------
def print_hello_world():
<FILL_ME>
-------------------- Output --------------------
def print_hello_world():
    print("Hello World")


def print_hello_world_with_name(name):
    print("Hello " + name)

```