Instructions to use K-intelligence/Midm-2.0-Mini-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use K-intelligence/Midm-2.0-Mini-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="K-intelligence/Midm-2.0-Mini-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("K-intelligence/Midm-2.0-Mini-Instruct")
model = AutoModelForCausalLM.from_pretrained("K-intelligence/Midm-2.0-Mini-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use K-intelligence/Midm-2.0-Mini-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "K-intelligence/Midm-2.0-Mini-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "K-intelligence/Midm-2.0-Mini-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/K-intelligence/Midm-2.0-Mini-Instruct

SGLang

How to use K-intelligence/Midm-2.0-Mini-Instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "K-intelligence/Midm-2.0-Mini-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "K-intelligence/Midm-2.0-Mini-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "K-intelligence/Midm-2.0-Mini-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "K-intelligence/Midm-2.0-Mini-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use K-intelligence/Midm-2.0-Mini-Instruct with Docker Model Runner:
```
docker model run hf.co/K-intelligence/Midm-2.0-Mini-Instruct
```

Midm-LLM commited on Jul 4, 2025

Commit

27c03f4

verified ·

1 Parent(s): f69e3f8

Update README.md

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@@ -13,7 +13,7 @@ library_name: transformers
 <p align="center">
 <br>
-    <span style="font-size: 60px; font-weight: bold;">Mi:dm 2.0-Mini</span>
 </br>
 </p>
 <p align="center">
@@ -59,11 +59,11 @@ library_name: transformers
 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.

 <p align="center">
 <br>
+    <span style="font-size: 60px; font-weight: bold;">Mi:dm 2.0 Mini</span>
 </br>
 </p>
 <p align="center">
 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.