language, license, tags, pipeline_tag

language	license	tags	pipeline_tag
ko en	apache-2.0	text-generation qwen2.5 korean instruct mlx	text-generation

Qwen2.5-7B-Instruct-kowiki-qa mlx convert model

• Original model is beomi/Qwen2.5-7B-Instruct-kowiki-qa

Requirement

• pip install mlx-lm

Usage

• Generate with CLI

  mlx_lm.generate --model mlx-community/Qwen2.5-7B-Instruct-kowiki-qa --prompt "하늘이 파란 이유가 뭐야?"
  

• In Python

  from mlx_lm import load, generate
  
  model, tokenizer = load(
      "mlx-community/Qwen2.5-7B-Instruct-kowiki-qa",
      tokenizer_config={"trust_remote_code": True},
  )
  
  prompt = "하늘이 파란 이유가 뭐야?"
  
  messages = [
      {"role": "system", "content": "당신은 친철한 챗봇입니다."},
      {"role": "user", "content": prompt},
  ]
  prompt = tokenizer.apply_chat_template(
      messages,
      tokenize=False,
      add_generation_prompt=True,
  )
  
  text = generate(
      model,
      tokenizer,
      prompt=prompt,
      # verbose=True,
      # max_tokens=8196,
      # temp=0.0,
  )
  

• OpenAI Compitable HTTP Server

  mlx_lm.server --model mlx-community/Qwen2.5-7B-Instruct-kowiki-qa --host 0.0.0.0
  

  import openai
  
  
  client = openai.OpenAI(
      base_url="http://localhost:8080/v1",
  )
  
  prompt = "하늘이 파란 이유가 뭐야?"
  
  messages = [
      {"role": "system", "content": "당신은 친절한 챗봇입니다.",},
      {"role": "user", "content": prompt},
  ]
  res = client.chat.completions.create(
      model='mlx-community/Qwen2.5-7B-Instruct-kowiki-qa',
      messages=messages,
      temperature=0.2,
  )
  
  print(res.choices[0].message.content)