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 3, 2025

Commit

4521806

verified ·

1 Parent(s): 6ec8317

Update README.md

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Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -18,7 +18,7 @@ library_name: transformers
 </p>
 <p align="center">
 🤗 <a href="">Mi:dm 2.0 Models</a> |
-📜 Mi:dm 2.0 Technical Report* |
 📕 Mi:dm 2.0 Technical Blog*
 </p>
@@ -521,7 +521,7 @@ You can try our model immediately via `Friendli.AI`. Simply click `Deploy` and t
 </p>
 ### Run on Your Local Machine
-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
 ### Deployment
@@ -533,7 +533,7 @@ vllm serve K-intelligence/Midm-2.0-Mini-Instruct
 ### Tutorials
-To help our end-users easily use Mi:dm 2.0, we have provided comprehensive tutorials on [github]().
 <br>
 <br>

 </p>
 <p align="center">
 🤗 <a href="">Mi:dm 2.0 Models</a> |
+📜 <a href="https://github.com/K-intelligence-Midm/Midm-2.0/blob/3a2cc5c63cee8534817c471603e703fc49470920/Mi_dm_2_0_technical_report.pdf">Mi:dm 2.0 Technical Report</a> |
 📕 Mi:dm 2.0 Technical Blog*
 </p>
 </p>
 ### Run on Your Local Machine
+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](https://github.com/K-intelligence-Midm/Midm-2.0) for more information
 ### Deployment
 ### Tutorials
+To help our end-users easily use Mi:dm 2.0, we have provided comprehensive tutorials on [github](https://github.com/K-intelligence-Midm/Midm-2.0).
 <br>
 <br>