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 Oct 29, 2025

Commit

f155867

verified ·

1 Parent(s): e87c58a

Update README.md

Browse files

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README.md +22 -1

README.md CHANGED Viewed

@@ -28,7 +28,7 @@ library_name: transformers
 # News 📢
-- 🔜 _(Coming Soon!) GGUF format model files will be available soon for easier local deployment._
 - 📕`2025/08/08`: Published a technical blog article about Mi:dm 2.0 Model.
 - ⚡️`2025/07/04`: Released Mi:dm 2.0 Model collection on Hugging Face🤗.
 <br>
@@ -527,11 +527,32 @@ We provide a detailed description about running Mi:dm 2.0 on your local machine
 ## Deployment
 To serve Mi:dm 2.0 using [vLLM](https://github.com/vllm-project/vllm)(`>=0.8.0`) with an OpenAI-compatible API:
 ```bash
 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](https://github.com/K-intelligence-Midm/Midm-2.0).

 # News 📢
+- 🔧`2025/10/29`: Added support for function calling on vLLM with Mi:dm 2.0 parser.
 - 📕`2025/08/08`: Published a technical blog article about Mi:dm 2.0 Model.
 - ⚡️`2025/07/04`: Released Mi:dm 2.0 Model collection on Hugging Face🤗.
 <br>
 ## Deployment
+#### Basic Serving
 To serve Mi:dm 2.0 using [vLLM](https://github.com/vllm-project/vllm)(`>=0.8.0`) with an OpenAI-compatible API:
 ```bash
 vllm serve K-intelligence/Midm-2.0-Mini-Instruct
 ```
+#### With Function Calling
+For advanced function calling tasks, you can serve Mi:dm 2.0 with our own tool parser:
+1. Download and place [Mi:dm 2.0 parser file](https://github.com/K-intelligence-Midm/Midm-2.0/blob/main/tutorial/03_open-webui/modelfile/midm_parser.py) in your working directory.
+2. Run the following Docker command to launch the vLLM server with our custom parser file:
+    ```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-Base-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:v0.8.0` and later, but we strongly recommend using `v0.11.0` for optimal stability and compatibility with our parser.
 ## 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).