Instructions to use bear7011/gemma4-e2b-webvid4K_FT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bear7011/gemma4-e2b-webvid4K_FT with Transformers:

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

pipe = pipeline("image-text-to-text", model="bear7011/gemma4-e2b-webvid4K_FT")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("bear7011/gemma4-e2b-webvid4K_FT")
model = AutoModelForImageTextToText.from_pretrained("bear7011/gemma4-e2b-webvid4K_FT")
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

vLLM

How to use bear7011/gemma4-e2b-webvid4K_FT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bear7011/gemma4-e2b-webvid4K_FT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bear7011/gemma4-e2b-webvid4K_FT",
		"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/bear7011/gemma4-e2b-webvid4K_FT

SGLang

How to use bear7011/gemma4-e2b-webvid4K_FT 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 "bear7011/gemma4-e2b-webvid4K_FT" \
    --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": "bear7011/gemma4-e2b-webvid4K_FT",
		"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 "bear7011/gemma4-e2b-webvid4K_FT" \
        --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": "bear7011/gemma4-e2b-webvid4K_FT",
		"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 bear7011/gemma4-e2b-webvid4K_FT with Docker Model Runner:
```
docker model run hf.co/bear7011/gemma4-e2b-webvid4K_FT
```

bear7011 commited on 3 days ago

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+---
+base_model: google/gemma-4-e2b-it
+library_name: transformers
+pipeline_tag: image-text-to-text
+---
+# gemma4-e4b-webvid4K_FT
+Full fine-tune of `google/gemma-4-e2b-it` on AI-generated video data derived from WebVid.
+## Training
+- Dataset: `bear7011/gemma-4-e4b-webvid-4K`
+- Samples: 3,941 video instruction examples
+- Method: full fine-tuning, no LoRA
+- Precision: bfloat16
+- GPUs: 4
+- DeepSpeed: ZeRO-3 with CPU optimizer and parameter offload
+- Epochs: 1
+- Global steps: 124
+- Per-device batch size: 1
+- Gradient accumulation steps: 8
+- Optimizer: AdamW
+- Learning rate: 5e-6
+- Projector learning rate: 5e-6
+- Image encoder learning rate: 0.0
+- Weight decay: 0.01
+- Warmup ratio: 0.03
+- LR scheduler: cosine
+- Gradient checkpointing: enabled
+- Max sequence length: 2304
+- Final training loss: 1.9510
+Checkpoints and training logs are not included in this repository.