Instructions to use Jaja07/gemma4-pruned-mi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jaja07/gemma4-pruned-mi with Transformers:

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

pipe = pipeline("image-text-to-text", model="Jaja07/gemma4-pruned-mi")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Jaja07/gemma4-pruned-mi")
model = AutoModelForMultimodalLM.from_pretrained("Jaja07/gemma4-pruned-mi")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Jaja07/gemma4-pruned-mi with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jaja07/gemma4-pruned-mi"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jaja07/gemma4-pruned-mi",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Jaja07/gemma4-pruned-mi

SGLang

How to use Jaja07/gemma4-pruned-mi 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 "Jaja07/gemma4-pruned-mi" \
    --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": "Jaja07/gemma4-pruned-mi",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "Jaja07/gemma4-pruned-mi" \
        --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": "Jaja07/gemma4-pruned-mi",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use Jaja07/gemma4-pruned-mi with Docker Model Runner:
```
docker model run hf.co/Jaja07/gemma4-pruned-mi
```

Gemma-4-E4B-it : Pruned with Mutual Information (30%)

Ce modèle est une version compressée (élaguée) de google/gemma-4-E4B-it. L'élagage a été réalisé de manière non supervisée en supprimant 30% des neurones des couches FFN (Feed-Forward Networks / MLP) en se basant sur une analyse de la redondance par Information Mutuelle.

Le modèle est sauvegardé au format safetensors avec les poids en bfloat16, et la configuration de l'architecture (intermediate_size pour la vision et le texte) a été mise à jour dynamiquement pour garantir une compatibilité totale avec vLLM.

🚀 Utilisation avec Transformers

Vous pouvez charger et utiliser ce modèle exactement comme le modèle Gemma 4 standard :

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Jaja07/gemma4-pruned-mi"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16
)

prompt = "Explique-moi ce qu'est le pruning de réseaux de neurones."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloads last month: 36

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for Jaja07/gemma4-pruned-mi

Base model

google/gemma-4-E4B

Finetuned

google/gemma-4-E4B-it

Finetuned

(221)

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