Instructions to use AMKCode/gemma-2-2b-it-q4f32_1-MLC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AMKCode/gemma-2-2b-it-q4f32_1-MLC with Transformers:

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

pipe = pipeline("text-generation", model="AMKCode/gemma-2-2b-it-q4f32_1-MLC")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("AMKCode/gemma-2-2b-it-q4f32_1-MLC", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AMKCode/gemma-2-2b-it-q4f32_1-MLC with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AMKCode/gemma-2-2b-it-q4f32_1-MLC"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AMKCode/gemma-2-2b-it-q4f32_1-MLC",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AMKCode/gemma-2-2b-it-q4f32_1-MLC

SGLang

How to use AMKCode/gemma-2-2b-it-q4f32_1-MLC 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 "AMKCode/gemma-2-2b-it-q4f32_1-MLC" \
    --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": "AMKCode/gemma-2-2b-it-q4f32_1-MLC",
		"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 "AMKCode/gemma-2-2b-it-q4f32_1-MLC" \
        --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": "AMKCode/gemma-2-2b-it-q4f32_1-MLC",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AMKCode/gemma-2-2b-it-q4f32_1-MLC with Docker Model Runner:
```
docker model run hf.co/AMKCode/gemma-2-2b-it-q4f32_1-MLC
```

gemma-2-2b-it-q4f32_1-MLC / README.md

AMKCode

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	---
	base_model: google/gemma-2-2b-it
	library_name: transformers
	license: gemma
	pipeline_tag: text-generation
	tags:
	- conversational
	- mlc-ai
	- MLC-Weight-Conversion
	extra_gated_heading: Access Gemma on Hugging Face
	extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and
	agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging
	Face and click below. Requests are processed immediately.
	extra_gated_button_content: Acknowledge license
	---

	# AMKCode/gemma-2-2b-it-q4f32_1-MLC
	This model was compiled using MLC-LLM with q4f32_1 quantization from [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it).
	The conversion was done using the [MLC-Weight-Conversion](https://huggingface.co/spaces/mlc-ai/MLC-Weight-Conversion) space.

	To run this model, please first install [MLC-LLM](https://llm.mlc.ai/docs/install/mlc_llm.html#install-mlc-packages).

	To chat with the model on your terminal:
	```bash
	mlc_llm chat HF://AMKCode/gemma-2-2b-it-q4f32_1-MLC
	```

	For more information on how to use MLC-LLM, please visit the MLC-LLM [documentation](https://llm.mlc.ai/docs/index.html).