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