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

# EEVE-Korean-Instruct-10.8B-v1.0-AWQ
- Model creator: [Yanolja](https://huggingface.co/yanolja)
- Original model: [yanolja/EEVE-Korean-Instruct-10.8B-v1.0](https://huggingface.co/yanolja/EEVE-Korean-Instruct-10.8B-v1.0)

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## Description

This repo contains AWQ model files for [yanolja/EEVE-Korean-Instruct-10.8B-v1.0](https://huggingface.co/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](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - Llama and Mistral models only
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code

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## Using OpenAI Chat API with vLLM

Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).

- 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

```shell
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:

```shell
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:

```python
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)
```
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