README.md

metadata

license: mit
language:
  - en
  - ko
tags:
  - KT
  - K-intelligence
  - Mi:dm
  - mlx
inference: true
pipeline_tag: text-generation
library_name: mlx
base_model: K-intelligence/Midm-2.0-Base-Instruct

litmudoc/Midm-2.0-Base-Instruct-MLX-Q8

This model litmudoc/Midm-2.0-Base-Instruct-MLX-Q8 was converted to MLX format from K-intelligence/Midm-2.0-Base-Instruct using mlx-lm version 0.25.3.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("litmudoc/Midm-2.0-Base-Instruct-MLX-Q8")

prompt = "hello"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)