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K-intelligence
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K-intelligence
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Mi:dm 2.0 is a "Korea-centric AI" model developed using KT's proprietary technology. The term "Korea-centric AI" refers to a model that deeply internalizes the unique values, cognitive frameworks, and commonsense reasoning inherent to Korean society. It goes beyond simply processing or generating Korean text—it reflects a deeper understanding of the socio-cultural norms and values that define Korean society.
Mi:dm 2.0 is released in two versions:
Mi:dm 2.0 Base
An 11.5B parameter dense model designed to balance model size and performance.
It extends an 8B-scale model by applying the Depth-up Scaling (DuS) method, making it suitable for real-world applications that require both performance and versatility.
Mi:dm 2.0 Mini
A lightweight 2.3B parameter dense model optimized for on-device environments and systems with limited GPU resources.
It was derived from the Base model through pruning and distillation to enable compact deployment.
[!Note] Neither the pre-training nor the post-training data includes KT users' data.
Here is the code snippet to run conversational inference with the model:
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig model_name = "K-intelligence/Midm-2.0-Mini-Instruct" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto")tokenizer = AutoTokenizer.from_pretrained(model_name)generation_config = GenerationConfig.from_pretrained(model_name) prompt = "KT에 대해 소개해줘" # message for inferencemessages = [ {"role": "system", "content": "Mi:dm(믿:음)은 KT에서 개발한 AI 기반 어시스턴트이다."}, {"role": "user", "content": prompt}] input_ids = tokenizer.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_tensors="pt") output = model.generate( input_ids.to("cuda"), generation_config=generation_config, eos_token_id=tokenizer.eos_token_id, max_new_tokens=128, do_sample=False,)print(tokenizer.decode(output[0]))[!NOTE] The
transformerslibrary should be version4.45.0or higher.
* indicates KT proprietary evaluation resources.
You can try our model immediately via Friendli.AI. Simply click Deploy and then Friendli Endpoints.
[!Note] Please note that a login to
Friendli.AIis required after your fifth chat interaction.
We provide a detailed description about running Mi:dm 2.0 on your local machine using llama.cpp, LM Studio, and Ollama. Please check our github for more information
To serve Mi:dm 2.0 using vLLM(>=0.8.0) with an OpenAI-compatible API:
bash
vllm serve K-intelligence/Midm-2.0-Mini-InstructFor advanced function calling tasks, you can serve Mi:dm 2.0 with our own tool parser:
bash
docker run --rm -it --gpus all -p 8000:8000 \ -e HUGGING_FACE_HUB_TOKEN="<YOUR_HUGGINGFACE_TOKEN>" \ -v "$(pwd)/midm_parser.py:/custom/midm_parser.py" \ vllm/vllm-openai:v0.11.0 \ --model K-intelligence/Midm-2.0-Mini-Instruct \ --enable-auto-tool-choice \ --tool-parser-plugin /custom/midm_parser.py \ --tool-call-parser midm-parser \ --host 0.0.0.0[!Note] This setup is compatible with
vllm/vllm-openai:v0.8.0and later, but we strongly recommend usingv0.11.0for optimal stability and compatibility with our parser.
To help our end-users easily use Mi:dm 2.0, we have provided comprehensive tutorials on github.
The training data for both Mi:dm 2.0 models consists primarily of English and Korean. Understanding and generation in other languages are not guaranteed.
The model is not guaranteed to provide reliable advice in fields that require professional expertise, such as law, medicine, or finance.
Researchers have made efforts to exclude unethical content from the training data — such as profanity, slurs, bias, and discriminatory language. However, despite these efforts, the model may still produce inappropriate expressions or factual inaccuracies.
Mi:dm 2.0 is licensed under the MIT License.
Mi:dm 2.0 Technical Inquiries: midm-llm@kt.com
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig model_name = "K-intelligence/Midm-2.0-Mini-Instruct" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto")tokenizer = AutoTokenizer.from_pretrained(model_name)generation_config = GenerationConfig.from_pretrained(model_name) prompt = "KT에 대해 소개해줘" # message for inferencemessages = [ {"role": "system", "content": "Mi:dm(믿:음)은 KT에서 개발한 AI 기반 어시스턴트이다."}, {"role": "user", "content": prompt}] input_ids = tokenizer.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_tensors="pt") output = model.generate( input_ids.to("cuda"), generation_config=generation_config, eos_token_id=tokenizer.eos_token_id, max_new_tokens=128, do_sample=False,)print(tokenizer.decode(output[0]))docker run --rm -it --gpus all -p 8000:8000 \ -e HUGGING_FACE_HUB_TOKEN="<YOUR_HUGGINGFACE_TOKEN>" \ -v "$(pwd)/midm_parser.py:/custom/midm_parser.py" \ vllm/vllm-openai:v0.11.0 \ --model K-intelligence/Midm-2.0-Mini-Instruct \ --enable-auto-tool-choice \ --tool-parser-plugin /custom/midm_parser.py \ --tool-call-parser midm-parser \ --host 0.0.0.0