Instructions to use Puujeeeeeeeeeeee/gemma4-e4b-cpt-round3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Puujeeeeeeeeeeee/gemma4-e4b-cpt-round3 with Transformers:

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

pipe = pipeline("image-text-to-text", model="Puujeeeeeeeeeeee/gemma4-e4b-cpt-round3")

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Puujeeeeeeeeeeee/gemma4-e4b-cpt-round3")
model = AutoModelForMultimodalLM.from_pretrained("Puujeeeeeeeeeeee/gemma4-e4b-cpt-round3", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Puujeeeeeeeeeeee/gemma4-e4b-cpt-round3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Puujeeeeeeeeeeee/gemma4-e4b-cpt-round3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Puujeeeeeeeeeeee/gemma4-e4b-cpt-round3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Puujeeeeeeeeeeee/gemma4-e4b-cpt-round3

SGLang

How to use Puujeeeeeeeeeeee/gemma4-e4b-cpt-round3 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 "Puujeeeeeeeeeeee/gemma4-e4b-cpt-round3" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Puujeeeeeeeeeeee/gemma4-e4b-cpt-round3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Puujeeeeeeeeeeee/gemma4-e4b-cpt-round3" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Puujeeeeeeeeeeee/gemma4-e4b-cpt-round3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Desktop
Docker Model Runner
How to use Puujeeeeeeeeeeee/gemma4-e4b-cpt-round3 with Docker Model Runner:
```
docker model run hf.co/Puujeeeeeeeeeeee/gemma4-e4b-cpt-round3
```

gemma4-e4b-cpt-round3 / README.md

Puujeeeeeeeeeeee

Unsloth Model Card

7a2d58c verified about 1 month ago

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metadata

base_model: Puujeeeeeeeeeeee/gemma4-e4b-cpt-round2
tags:
  - text-generation-inference
  - transformers
  - unsloth
  - gemma4
license: apache-2.0
language:
  - en

Uploaded finetuned model

Developed by: Puujeeeeeeeeeeee
License: apache-2.0
Finetuned from model : Puujeeeeeeeeeeee/gemma4-e4b-cpt-round2

This gemma4 model was trained 2x faster with Unsloth and Huggingface's TRL library.