> ## Documentation Index
> Fetch the complete documentation index at: https://docs.inferless.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Deploy OpenLLM-leaderboard topper Smaug-72B using Inferless
> This tutorial demonstrates deploying a quantized Smaug-72B model using vLLM. We will be deploying a 4-bit quantized GPTQ version of this model.
## Introduction
Smaug-72B - which is a current topper of the Hugging Face LLM leaderboard and it’s the first model with an average score of 80. Smaug-72B is finetuned directly from [MoMo-72B-lora-1.8.7-DPO](https://huggingface.co/moreh/MoMo-72B-lora-1.8.7-DPO) and is ultimately based on [Qwen-72B](https://huggingface.co/Qwen/Qwen-72B).
## Our Observations
We have deployed the [4-bit quantized version](https://huggingface.co/LoneStriker/Smaug-72B-v0.1-GPTQ) of the model using vLLM on an A100 GPU(80GB). Here are our observations:
| Inference Time | Cold Start Time | Token/Sec | Latency/Token | VRAM Required |
| -------------- | --------------- | --------- | ------------- | ------------- |
| 8.01 sec | 26.65 sec | 29.94 | 33.39 ms | 69.19 GB |
## Defining Dependencies
We are using the [vLLM library](https://github.com/vllm-project/vllm), which boost the inference speed of the LLM. We will deploy a GPTQ 4bit quantized version of the model.
## Constructing the GitHub/GitLab Template
Now quickly construct the GitHub/GitLab template, this process is mandatory and make sure you don't add any file named `model.py`.
```
Smaug-72B/
├── app.py
├── inferless-runtime-config.yaml
├── inferless.yaml
└── input_schema.py
```
You can also add other files to this directory.
## Create the class for inference
In the [app.py](https://github.com/inferless/Smaug-72B/blob/main/app.py) we will define the class and import all the required functions
1. `def initialize`: In this function, you will initialize your model and the required `variables`. You can adjust the `gpu_memory_utilization` parameter to reduce GPU usage.
2. `def infer`: This function gets called for every request that you send. Here you can define all the steps that are required for the inference. You can also pass custom values for inference and pass it through `inputs(dict)` parameter.
3. `def finalize`: This function cleans up all the allocated memory.
```python theme={null}
from vllm import LLM, SamplingParams
import inferless
app = inferless.Cls(gpu="A100")
class InferlessPythonModel:
@app.load
def initialize(self):
model_id = "LoneStriker/Smaug-72B-v0.1-GPTQ"
self.sampling_params = SamplingParams(temperature=0.7, top_p=0.95,max_tokens=256)
self.llm = LLM(model=model_id, quantization="gptq", dtype="float16",max_model_len=2048,gpu_memory_utilization=0.9)
@app.infer
def infer(self, inputs):
prompts = inputs["prompt"]
result = self.llm.generate(prompts, self.sampling_params)
result_output = [output.outputs[0].text for output in result]
return {'gresult': result_output[0]}
def finalize(self):
self.llm = None
```
## Create the Input Schema
We have to create a `input_schema.py` in your GitHub/Gitlab repository this will help us create the Input parameters. You can checkout our documentation on [Input / Output Schema](https://docs.inferless.com/model-import/input-output-schema).
For this tutorial, we have defined a parameter `prompt` which is required during the API call. Now lets create the [input\_schema.py](https://github.com/inferless/Smaug-72B/blob/main/input%5Fschema.py).
```
INPUT_SCHEMA = {
"prompt": {
'datatype': 'STRING',
'required': True,
'shape': [1],
'example': ["What is quantization?"]
}
}
```
## Creating the Custom Runtime
This is a mandatory step where we allow the users to upload their custom runtime through [inferless-runtime-config.yaml](https://github.com/inferless/Smaug-72B/blob/main/inferless-runtime-config.yaml).
```
build:
cuda_version: "12.1.1"
python_packages:
- "vllm==0.3.1"
- "inferless-cli==2.0.9"
- "hf-transfer==0.1.9"
- "huggingface-hub==0.27.1"
```
## Test your model with Remote Run
You can use the `inferless remote-run`([installation guide here](https://docs.inferless.com/model-import/cli-import#cli-import)) command to test your model or any custom Python script in a remote GPU environment directly from your local machine. Make sure that you use `Python3.10` for seamless experience.
### Step 1: Add the Decorators and local entry point
To enable **Remote Run**, simply do the following:
1. Import the `inferless` library and initialize `Cls(gpu="A100")`. The available GPU options are `T4`, `A10` and `A100`.
2. Decorated the `initialize` and `infer` functions with `@app.load` and `@app.infer` respectively.
3. Create the Local Entry Point by decorating a function (for example, `my_local_entry`) with `@inferless.local_entry_point`.
Within this function, instantiate your model class, convert any incoming parameters into a `RequestObjects` object, and invoke the model's `infer` method.
```python theme={null}
from vllm import LLM, SamplingParams
import inferless
from pydantic import BaseModel, Field
from typing import Optional
@inferless.request
class RequestObjects(BaseModel):
prompt: str = Field(default="Explain Deep Learning.")
temperature: Optional[float] = 0.7
top_p: Optional[float] = 0.1
repetition_penalty: Optional[float] = 1.18
top_k: Optional[int] = 40
max_tokens: Optional[int] = 256
@inferless.response
class ResponseObjects(BaseModel):
generated_text: str = Field(default='Test output')
app = inferless.Cls(gpu="A100")
class InferlessPythonModel:
@app.load
def initialize(self):
model_id = "LoneStriker/Smaug-72B-v0.1-GPTQ"
self.llm = LLM(model=model_id, quantization="gptq", dtype="float16",max_model_len=2048,gpu_memory_utilization=0.9)
@app.infer
def infer(self, request: RequestObjects) -> ResponseObjects:
sampling_params = SamplingParams(temperature=request.temperature,top_p=request.top_p,repetition_penalty=request.repetition_penalty,
top_k=request.top_k,max_tokens=request.max_tokens)
result = self.llm.generate(request.prompt, sampling_params)
result_output = [output.outputs[0].text for output in result]
generateObject = ResponseObjects(generated_text = result_output[0])
return generateObject
def finalize(self):
self.llm = None
@inferless.local_entry_point
def my_local_entry(dynamic_params):
request_objects = RequestObjects(**dynamic_params)
model_instance = InferlessPythonModel()
return model_instance.infer(request_objects)
```
### Step 2: Run with Remote GPU
From your local terminal, navigate to the folder containing your `app.py` and your `inferless-runtime-config.yaml` and run:
```bash theme={null}
inferless remote-run app.py -c inferless-runtime-config.yaml --prompt "What is quantization?"
```
You can pass the other input parameters in the same way as long as your code expects them in the `inputs` dictionary.
If you want to exclude certain files or directories from being uploaded, use the `--exclude` or `-e` flag.
## Method A: Deploying the model on Inferless Platform
Inferless supports multiple ways of [importing your model](https://docs.inferless.com/model-import/file-structure-req/file-structure-requirements). For this tutorial, we will use GitHub.
### Step 1: Login to the inferless dashboard can click on Import model button
Navigate to your desired workspace in Inferless and Click on `Add a custom model` button that you see on the top right. An import wizard will open up.
### Step 2: Follow the UI to complete the model Import
* Select the GitHub/GitLab Integration option to connect your source code repository with the deployment environment.
* Navigate to the specific GitHub repository that contains your model's code. Here, you will need to identify and enter the name of the model you wish to import.
* Choose the appropriate type of machine that suits your model's requirements. Additionally, specify the minimum and maximum number of replicas to define the scalability range for deploying your model.
* Optionally, you have the option to enable automatic build and deployment. This feature triggers a new deployment automatically whenever there is a new code push to your repository.
* If your model requires additional software packages, configure the Custom Runtime settings by including necessary pip or apt packages. Also, set up environment variables such as Inference Timeout, Container Concurrency, and Scale Down Timeout to tailor the runtime environment according to your needs.
* Wait for the validation process to complete, ensuring that all settings are correct and functional. Once validation is successful, click on the "Import" button to finalize the import of your model.
### Step 3: Wait for the model build to complete usually takes \~5-10 minutes
### Step 4: Use the APIs to call the model
Once the model is in 'Active' status you can click on the 'API' page to call the model
### Here is the Demo:
## Method B: Deploying the model on Inferless CLI
Inferless allows you to deploy your model using Inferless-CLI. Follow the steps to deploy using Inferless CLI.
### Clone the repository of the model
Let's begin by cloning the model repository:
```bash theme={null}
git clone https://github.com/inferless/Smaug-72B.git
```
### Deploy the Model
To deploy the model using Inferless CLI, execute the following command:
```bash theme={null}
inferless deploy --gpu A100 --runtime inferless-runtime-config.yaml
```
**Explanation of the Command:**
* `--gpu A100`: Specifies the GPU type for deployment. Available options include `A10`, `A100`, and `T4`.
* `--runtime inferless-runtime-config.yaml`: Defines the runtime configuration file. If not specified, the default Inferless runtime is used.
