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)

# 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 / params_shard_13.bin

AMKCode

Add files using upload-large-folder tool

beec542 verified almost 2 years ago

Download

History Blame Contribute Delete

33.2 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 6521610731bbca00dfe07879e8884fe05f0dc9710fc329121e621080e8044864
Size of remote file:: 33.2 MB
SHA256:: da94d287dcced52a7ee1749f3ddd7c526d2017d01c2c7ac4fb009aa9b9309f9c

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.