🐣 The Quantized LLaMA 3.3 70B Instruct Model

Original Base Model: meta-llama/Llama-3.3-70B-Instruct.
Link: https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct

🚀 Model Inference

from vllm import LLM, SamplingParams


# Model name and hyperparameters
model_name = "shuyuej/Llama-3.3-70B-Instruct-GPTQ"
num_gpus_vllm = 4  # The number of GPUs you wanna use
gpu_utilization_vllm = 0.95  # The GPU utilization (from 0 to 1)
max_model_len_vllm = 2048  # The maximum input token length
max_new_tokens = 1024  # The maximum number of generated tokens

# The input prompts and sampling parameters for text generation
prompts = [
    "Hello, my name is",
    "The president of the United States is",
    "The capital of France is",
    "The future of AI is",
]
sampling_params = SamplingParams(
    temperature=0,
    top_p=1,
    max_tokens=max_new_tokens,
)

# Initialize vLLM engine
llm = LLM(
    model=model_name,
    tokenizer=model_name,
    # While using the GPTQ quantization, the current vLLM only supports float16, as of Dec. 14th, 2024
    dtype='float16',
    quantization="GPTQ",
    # Acknowledgement: Benjamin Kitor
    # https://github.com/vllm-project/vllm/issues/2794
    # Reference: https://github.com/vllm-project/vllm/issues/1908
    distributed_executor_backend="mp",
    tensor_parallel_size=num_gpus_vllm,
    gpu_memory_utilization=gpu_utilization_vllm,
    # Note: We add this only to save the GPU Memories!
    max_model_len=max_model_len_vllm,
    disable_custom_all_reduce=True,
    enable_lora=False,
)

# Generate responses using the vLLM LLM
completions = llm.generate(prompts, sampling_params)
for output in completions:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

🔥 Real-world deployment

For real-world deployment, please refer to the vLLM Distributed Inference and Serving and OpenAI Compatible Server.

vLLM can be deployed as a server that implements the OpenAI API protocol. This allows vLLM to be used as a drop-in replacement for applications using OpenAI API. By default, it starts the server at http://localhost:8000.

vllm serve shuyuej/Llama-3.3-70B-Instruct-GPTQ \
    --quantization gptq \
    --trust-remote-code \
    --dtype float16 \
    --max-model-len 4096 \
    --distributed-executor-backend mp \
    --pipeline-parallel-size 4 \
    --api-key token-abc123

Please check here if you wanna change Engine Arguments.

If you would like to deploy your LoRA adapter, please refer to the vLLM documentation for a detailed guide.
It provides step-by-step instructions on how to serve LoRA adapters effectively in a vLLM environment.
We have also shared our trained LoRA adapter here. Please download it manually if needed.

git clone https://huggingface.co/shuyuej/Public-Shared-LoRA-for-Llama-3.3-70B-Instruct-GPTQ

Then, use the vLLM to serve the base model with the LoRA adapter by including the --enable-lora flag and specifying --lora-modules:

vllm serve shuyuej/Llama-3.3-70B-Instruct-GPTQ \
    --quantization gptq \
    --trust-remote-code \
    --dtype float16 \
    --max-model-len 4096 \
    --distributed-executor-backend mp \
    --pipeline-parallel-size 4 \
    --api-key token-abc123 \
    --enable-lora \
    --lora-modules adapter=Public-Shared-LoRA-for-Llama-3.3-70B-Instruct-GPTQ/checkpoint-18640

Since this server is compatible with OpenAI API, you can use it as a drop-in replacement for any applications using OpenAI API. For example, another way to query the server is via the openai python package:

#!/usr/bin/env python
# coding=utf-8

import time
import asyncio

from openai import AsyncOpenAI

# Our system prompt
SYSTEM_PROMPT = (
    "I am PodGPT, a large language model developed by the Kolachalama Lab in Boston, "
    "specializing in science, technology, engineering, mathematics, and medicine "
    "(STEMM)-related research and education, powered by podcast audio.\n"
    "I provide information based on established scientific knowledge but must not offer "
    "personal medical advice or present myself as a licensed medical professional.\n"
    "I will maintain a consistently professional and informative tone, avoiding humor, "
    "sarcasm, and pop culture references.\n"
    "I will prioritize factual accuracy and clarity while ensuring my responses are "
    "educational and non-harmful, adhering to the principle of 'do no harm'.\n"
    "My responses are for informational purposes only and should not be considered a "
    "substitute for professional consultation."
)

# Initialize the AsyncOpenAI client
client = AsyncOpenAI(
    base_url="http://localhost:8000/v1",
    api_key="token-abc123",
)


async def main(message):
    """
    Streaming responses with async usage and "await" with each API call:
    Reference: https://github.com/openai/openai-python?tab=readme-ov-file#streaming-responses
    :param message: The user query
    """
    start_time = time.time()
    stream = await client.chat.completions.create(
        model="shuyuej/Llama-3.3-70B-Instruct-GPTQ",
        messages=[
            {
                "role": "system",
                "content": SYSTEM_PROMPT,
            },
            {
                "role": "user",
                "content": message,
            }
        ],
        max_tokens=2048,
        temperature=0.2,
        top_p=1,
        stream=True,
        extra_body={
            "ignore_eos": False,
            # https://huggingface.co/shuyuej/Llama-3.3-70B-Instruct-GPTQ/blob/main/config.json#L10-L14
            "stop_token_ids": [128001, 128008, 128009],
        },
    )

    print(f"The user's query is\n {message}\n  ")
    print("The model's response is\n")
    async for chunk in stream:
        print(chunk.choices[0].delta.content or "", end="")
    print(f"\nInference time: {time.time() - start_time:.2f} seconds\n")
    print("=" * 100)


if __name__ == "__main__":
    # Some random user queries
    prompts = [
        "Hello, my name is",
        "The president of the United States is",
        "The capital of France is",
        "The future of AI is",
        "Can you tell me more about Bruce Lee?",
        "What are the differences between DNA and RNA?",
        "What is dementia and Alzheimer's disease?",
        "Tell me the differences between Alzheimer's disease and dementia"
    ]
    
    # Conduct model inference
    for message in prompts:
        asyncio.run(main(message=message))
        print("\n\n")

Here is a demo of the real-world model inference and deployment

🐤 Single Checkpoint

Please note that if you need a single checkpoint (39.8 GB) instead of sharded checkpoints (each one is 5.34 GB), you can use revision="f77c1b3".
Here are our model loader codes for fine-tuning a LoRA adapter: https://github.com/vkola-lab/PodGPT/blob/main/lib/model_loader_quantization.py.
Also, please check our inference codes if you are interested: https://github.com/vkola-lab/PodGPT/blob/main/utils/eval_utils.py#L75-L98.

📖 Quantization Configurations

This model was quantized using the GPTQModel library. The source codes to build this quantized model are available here.

"quantization_config": {
    "bits": 4,
    "checkpoint_format": "gptq",
    "desc_act": true,
    "dynamic": null,
    "group_size": 128,
    "lm_head": false,
    "meta": {
      "damp_auto_increment": 0.0015,
      "damp_percent": 0.01,
      "quantizer": [
        "gptqmodel:1.4.0-dev"
      ],
      "static_groups": false,
      "true_sequential": true,
      "uri": "https://github.com/modelcloud/gptqmodel"
    },
    "quant_method": "gptq",
    "sym": true
  },

⛏️ Source Codes

Source Codes for quantization:
https://github.com/vkola-lab/medpodgpt/tree/main/quantization.
Source Codes for training the LoRA adapter:
https://github.com/vkola-lab/PodGPT/blob/main/lib/model_loader_quantization.py.
Source Codes for inference (without LoRA and with LoRA):
https://github.com/vkola-lab/PodGPT/blob/main/utils/vllm_utils.py#L61-L121.