SOLAR-10.7B, an advanced large language model (LLM) with 10.7 billion parameters, demonstrates superior performance in various natural language processing (NLP) tasks. They have presented a methodology for scaling LLMs called depth up-scaling (DUS), which encompasses architectural modifications and continued pretraining. They have integrated Mistral 7B weights into the upscaled layers, and finally, continued pre-training for the entire model.

Our Observations

We utilized AutoGPTQ to quantize SOLAR-10.7B-Instruct-v1.0 into a 4-bit quantized GPTQ version. In the inference process, we deployed the quantized model on an A100 GPU (80GB) using vLLM. We also tried deploying via Auto-GPTQ as mentioned in our observations below:

LibraryInference TimeCold Start TimeToken/SecLatency/TokenVRAM Required
vLLM (Recommended)1.37 sec11.69 sec111.548.97 ms69.33 GB
Auto-GPTQ27.09 sec61.31 sec9.82101.98 ms5.67 GB

Getting Started with Quantization

Quantization techniques reduces the model’s computation cost and memory. It represent the model’s weights and activations in lower precision data-types while trying not to reduce in the accuracy.

We have quantized the model using GPTQ algorithm, GPTQ is a quantization algorithms for LLMs. We have used AutoGPTQ for 4-bit GPTQ quantization.

Install the AutoGPTQ library:

pip install auto-gptq

Import the following libraries, and initialize the model and the tokenizer. For GPTQ calibration phase we are using VMware/open-instruct dataset.

from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
from transformers import AutoTokenizer
from datasets import load_dataset
import random
import numpy as np
import torch

model_id = "upstage/SOLAR-10.7B-Instruct-v1.0"
quantized_model_dir = "SOLAR-10.7B-Instruct-v1.0"

tokenizer = AutoTokenizer.from_pretrained("upstage/SOLAR-10.7B-Instruct-v1.0", use_fast=True)

train_data = load_dataset('VMware/open-instruct')
tokenized_data = tokenizer("\n\n".join(train_data['train']['response']), return_tensors='pt')

def generate_data(nsamples,seed,seqlen):

    train_dataset = []

    for _ in range(nsamples):
        i = random.randint(0, tokenized_data.input_ids.shape[1] - seqlen - 1)
        j = i + seqlen
        inp = tokenized_data.input_ids[:, i:j]
        attention_mask = torch.ones_like(inp)
        train_dataset.append({'input_ids':inp,'attention_mask': attention_mask})
    return train_dataset

Now you can start the quantization process, it will create a new directory where it will store the quantized model. The quantized model is 5.98 GB, which is approximately 27.85% of the original model 21.47 GB. Here’s the link to our quanitized model.

training_dataset = generate_data(1000,4040,2048)
quantize_config = BaseQuantizeConfig(

model = AutoGPTQForCausalLM.from_pretrained(model_id,quantize_config)

model.save_quantized(quantized_model_dir, use_safetensors=True)

Defining Dependencies

We are using the vLLM library, which enables you to run LLM on low memory. We 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

└── inferless-runtime-config.yaml

You can also add other files to this directory.

Create the class for inference

In the we will define the class and import all the required functions

  1. def initialize: In this function, you will initialize your model and define any variable that you want to use during inference.

  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.

from vllm import LLM, SamplingParams

class InferlessPythonModel:
    def initialize(self):

        self.sampling_params = SamplingParams(temperature=0.7, top_p=0.95,max_tokens=256)
        self.llm = LLM(model="Inferless/SOLAR-10.7B-Instruct-v1.0-GPTQ", quantization="gptq", dtype="float16")

    def infer(self, inputs):
        prompts = inputs["prompt"]
        result = self.llm.generate(prompts, self.sampling_params)
        result_output = [[[output.outputs[0].text,output.outputs[0].token_ids] for output in result]

        return {'generated_result': result_output[0]}

    def finalize(self):

Creating the Custom Runtime

This is a mandatory step where we allow the users to upload their custom runtime through inferless-runtime-config.yaml.

  cuda_version: "12.1.1"
    - "libssl-dev"
    - "torch==2.1.2"
    - "vllm==0.2.6"
    - "transformers==4.36.2"
    - "accelerate==0.25.0"

Method A: Deploying the model on Inferless Platform

Selection of the framework

Inferless offers comprehensive support for all the major machine learning frameworks. In the first step, it is mandatory to designate the framework for your model. For the purposes of this tutorial, we will opt for PyTorch.

Import the Model through GitHub

Inferless supports multiple ways of importing your model. For this tutorial, we will use GitHub.

Click on theRepo(Custom code) and then click on the Add provider to connect to your GitHub account. Once your account integration is completed, click on your Github account and continue.

Provide the Model details

Enter the name of the model and pass the GitHub repository URL.

Now, you can define the model’s input and output parameters in JSON format. For more information, go through this page.

  • INPUT: Refer to the function def infer(self, inputs), here we mentioned inputs["prompt"], which means inputs the parameter will have a key prompt.

  • OUTPUT: The same goes here, the function mentioned above will return the results as a key-value pair return {"generated_result": result}.

Input JSON must include prompt a key to pass the instruction. For output JSON, it must be included generated_result to retrieve the output.

Configure the machine

In this 4th step, user have to configure the inference setup.On the Inferless platform, we support all the GPUs. For this tutorial, we recommend using A100 GPU. Select A100 from the drop-down menu in the GPU Type.

You also have the flexibility to select from different machine types. Opting for a dedicated machine type will grant you access to an entire GPU, while choosing the shared option allocates 50% of the VRAM. In this tutorial, we will opt for the dedicated machine type.

Choose the Minimum and Maximum replicas that you would need for your model:

  • Min replica: The number of inference workers to keep on at all times.

  • Max replica: The maximum number of inference workers to allow at any point of time.

If you wish to enable Automatic rebuild for your model setup, toggle the switch. Note that setting up a web-hook is necessary for this feature. Click here for more details.

In the Advance configuration, we have the option to select the custom runtime. First, click on the Add runtime to upload the inferless-runtime-config.yaml file, give any name and save it. Choose the runtime from the drop-down menu and then click on continue.

Review and Deploy

In this final stage, carefully review all modifications. Once you’ve examined all changes, proceed to deploy the model by clicking the Submit button.

Voilà, your model is now deployed!

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.

Initialization of the model

Create the and inferless-runtime-config.yaml, move the files to the working directory. Run the following command to initialize your model:

inferless init

Changes in Input and Output

Adjust the input and output keys in the JSON configuration files. For example:

    "inputs": [
        "data": [
          "What is an AI?"
        "name": "prompt",
        "shape": [
        "datatype": "BYTES"
    "outputs": [
        "data": [
        "name": "generated_result",
        "shape": [
        "datatype": "BYTES"

Upload the custom runtime

Once you have created the inferless-runtime-config.yaml file, you can run the following command:

inferless runtime upload

Upon entering this command, you will be prompted to provide the configuration file name. Enter the name and ensure to update it in the inferless.yaml file. Now you are ready for the deployment.

Deploy the Model

Execute the following command to deploy your model. Once deployed, you can track the build logs on the Inferless platform:

inferless deploy