base_model, inference, language, library_name, license, pipeline_tag

base_model	inference	language	library_name	license	pipeline_tag
yanolja/EEVE-Korean-Instruct-10.8B-v1.0	false	ko	transformers	apache-2.0	text-generation

EEVE-Korean-Instruct-10.8B-v1.0-AWQ

• Model creator: Yanolja
• Original model: yanolja/EEVE-Korean-Instruct-10.8B-v1.0

Description

This repo contains AWQ model files for yanolja/EEVE-Korean-Instruct-10.8B-v1.0.

About AWQ

AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.

It is supported by:

• Text Generation Webui - using Loader: AutoAWQ
• vLLM - Llama and Mistral models only
• Hugging Face Text Generation Inference (TGI)
• Transformers version 4.35.0 and later, from any code or client that supports Transformers
• AutoAWQ - for use from Python code

Using OpenAI Chat API with vLLM

Documentation on installing and using vLLM can be found here.

• Please ensure you are using vLLM version 0.2 or later.
• When using vLLM as a server, pass the --quantization awq parameter.

Start the 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

python3 -m vllm.entrypoints.openai.api_server --model Copycats/EEVE-Korean-Instruct-10.8B-v1.0-AWQ --quantization awq --dtype half

• --model: huggingface model path
• --quantization: ”awq”
• --dtype: “half” for FP16. Recommended for AWQ quantization.

Querying the model using OpenAI Chat API:

• You can use the create chat completion endpoint to communicate with the model in a chat-like interface:

curl http://localhost:8000/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "Copycats/EEVE-Korean-Instruct-10.8B-v1.0-AWQ",
        "messages": [
            {"role": "system", "content": "당신은 사용자의 질문에 친절하게 답변하는 어시스턴트입니다."},
            {"role": "user", "content": "괜스레 슬퍼서 눈물이 나면 어떻게 하나요?"}
        ]
    }'

Python Client Example:

• Using the openai python package, you can also communicate with the model in a chat-like manner:

from openai import OpenAI
# Set OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"

client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)

chat_response = client.chat.completions.create(
    model="Copycats/EEVE-Korean-Instruct-10.8B-v1.0-AWQ",
    messages=[
        {"role": "system", "content": "당신은 사용자의 질문에 친절하게 답변하는 어시스턴트입니다."},
        {"role": "user", "content": "괜스레 슬퍼서 눈물이 나면 어떻게 하나요?"},
    ]
)
print("Chat response:", chat_response)