jinkyeongk/Midm-2.0-Base-Instruct-AWQ

Midm-2.0-Base-Instruct - AWQ 4-bit Quantized Version

This repository contains the AWQ (Activation-aware Weight Quantization) 4-bit quantized version of the K-intelligence/Midm-2.0-Base-Instruct model by KT AI.

This model is the result of a journey to solve real-world performance and cost issues encountered in a production environment. I hope this experience can be a practical guide for other developers facing similar challenges.

Model Details

• Base Model: K-intelligence/Midm-2.0-Base-Instruct
• Quantization Method: AWQ (Activation-aware Weight Quantization)
• Quantization Config:
  • w_bit: 4
  • q_group_size: 128
  • zero_point: True
• Library: AutoAWQ

⚙️ How to Get Started

To use this model, you will need to install the transformers, accelerate, and autoawq libraries.

pip install transformers accelerate autoawq
Usage Example
Python

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "jinkyeongk/Midm-2.0-Base-Instruct-AWQ"

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.float16
).eval()

# Construct the chat prompt
messages = [
    {"role": "user", "content": "Who are you?"}
]
input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)

# Generate a response
outputs = model.generate(input_ids, max_new_tokens=512, do_sample=True, temperature=0.7)
response = tokenizer.decode(outputs[0][input_ids.shape[1]:], skip_special_tokens=True)

print(response)

📊 Quantization Evaluation

To measure the performance degradation from quantization, the original (FP16) and quantized (AWQ) models were evaluated against two major Korean benchmarks.

• Ko-Best: Measures objective knowledge and reasoning skills (Accuracy).

• Ko-MTBench: Measures subjective conversational ability (Scores graded by GPT-4o as a judge).

Final Evaluation Results

Model	Benchmark	Metric	Score / Accuracy
K-intelligence/Midm-2.0-Base-Instruct (FP16)	skt/kobest_v1	hellaswag (Accuracy)	0.4900
jinkyeongk/Midm-2.0-Base-Instruct-AWQ (AWQ)	skt/kobest_v1	hellaswag (Accuracy)	0.4800
K-intelligence/Midm-2.0-Base-Instruct (FP16)	LGAI-EXAONE/KoMT-Bench	Avg. Score (by GPT-4o)	8.50 / 10.0
jinkyeongk/Midm-2.0-Base-Instruct-AWQ (AWQ)	LGAI-EXAONE/KoMT-Bench	Avg. Score (by GPT-4o)	6.40 / 10.0

Analysis

The results from the Ko-Best (hellaswag) benchmark show that the performance drop in objective reasoning ability due to AWQ 4-bit quantization was a mere 1.0 percentage point, which is a negligible decrease.

However, in the Ko-MTBench subjective evaluation using GPT-4o as a judge, a more significant performance drop of 2.1 points on average was observed.

This suggests that while AWQ quantization maintains performance on well-defined, knowledge-based tasks like multiple-choice questions (Ko-Best), it can lead to some loss in nuance, expressiveness, or the sophistication of reasoning in more open-ended, conversational tasks (Ko-MTBench).

Therefore, this quantized model offers a massive improvement in speed and cost-efficiency at the expense of a slight trade-off in creative or complex conversational abilities. Users should consider this trade-off based on their specific application.