Instructions to use chimbiwide/Gemma3NPC-it-beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chimbiwide/Gemma3NPC-it-beta with Transformers:

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

pipe = pipeline("image-text-to-text", model="chimbiwide/Gemma3NPC-it-beta")
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("chimbiwide/Gemma3NPC-it-beta")
model = AutoModelForMultimodalLM.from_pretrained("chimbiwide/Gemma3NPC-it-beta", device_map="auto")
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 chimbiwide/Gemma3NPC-it-beta with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "chimbiwide/Gemma3NPC-it-beta"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chimbiwide/Gemma3NPC-it-beta",
		"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/chimbiwide/Gemma3NPC-it-beta

SGLang

How to use chimbiwide/Gemma3NPC-it-beta 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 "chimbiwide/Gemma3NPC-it-beta" \
    --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": "chimbiwide/Gemma3NPC-it-beta",
		"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 "chimbiwide/Gemma3NPC-it-beta" \
        --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": "chimbiwide/Gemma3NPC-it-beta",
		"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"
						}
					}
				]
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use chimbiwide/Gemma3NPC-it-beta with Docker Model Runner:
```
docker model run hf.co/chimbiwide/Gemma3NPC-it-beta
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Gemma3NPC-it-beta

A test model with less convervative training parameters

As mentioned in our original article, we employed a very conservative training parameters for Gemma3NPC

Ever since then, we have always wanted to test the performance of the model when we make the training parameters less conservative.

So we present Gemma3NPC-it-beta.

Check out our training notebook here

Training parameters compared to `Gemma3NPC-it`

Parameter	Gemma3NPC-it	Gemma3NPC-it-beta
Learning Rate	2e-5	2.5e-5 (+25%)
Warmup Steps	800	100
gradient clipping	0.4	1.0

Here is a graph of the Step Training Loss, saved every 10 steps:

Downloads last month: 12

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for chimbiwide/Gemma3NPC-it-beta

Quantizations

2 models

Dataset used to train chimbiwide/Gemma3NPC-it-beta

Space using chimbiwide/Gemma3NPC-it-beta 1

Collection including chimbiwide/Gemma3NPC-it-beta

Gemma3NPC-it-beta

Collection

A new attempt at training Gemma3NPC, with less conservative training parameters • 3 items • Updated Feb 27

Gemma3NPC-it-beta

A test model with less convervative training parameters

Training parameters compared to Gemma3NPC-it

Model tree for chimbiwide/Gemma3NPC-it-beta

Dataset used to train chimbiwide/Gemma3NPC-it-beta

Space using chimbiwide/Gemma3NPC-it-beta 1

Collection including chimbiwide/Gemma3NPC-it-beta

Training parameters compared to `Gemma3NPC-it`