Instructions to use peft-internal-testing/tiny-random-gemma4-E2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use peft-internal-testing/tiny-random-gemma4-E2B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="peft-internal-testing/tiny-random-gemma4-E2B")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("peft-internal-testing/tiny-random-gemma4-E2B") model = AutoModelForImageTextToText.from_pretrained("peft-internal-testing/tiny-random-gemma4-E2B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use peft-internal-testing/tiny-random-gemma4-E2B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "peft-internal-testing/tiny-random-gemma4-E2B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "peft-internal-testing/tiny-random-gemma4-E2B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/peft-internal-testing/tiny-random-gemma4-E2B
- SGLang
How to use peft-internal-testing/tiny-random-gemma4-E2B 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 "peft-internal-testing/tiny-random-gemma4-E2B" \ --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": "peft-internal-testing/tiny-random-gemma4-E2B", "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 "peft-internal-testing/tiny-random-gemma4-E2B" \ --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": "peft-internal-testing/tiny-random-gemma4-E2B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use peft-internal-testing/tiny-random-gemma4-E2B with Docker Model Runner:
docker model run hf.co/peft-internal-testing/tiny-random-gemma4-E2B
File size: 1,528 Bytes
42534d7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | {
"audio_token": "<|audio|>",
"backend": "tokenizers",
"boa_token": "<|audio>",
"boi_token": "<|image>",
"bos_token": "<bos>",
"eoa_token": "<audio|>",
"eoc_token": "<channel|>",
"eoi_token": "<image|>",
"eos_token": "<eos>",
"eot_token": "<turn|>",
"escape_token": "<|\"|>",
"etc_token": "<tool_call|>",
"etd_token": "<tool|>",
"etr_token": "<tool_response|>",
"extra_special_tokens": [
"<|video|>"
],
"image_token": "<|image|>",
"is_local": false,
"local_files_only": false,
"mask_token": "<mask>",
"model_max_length": 1000000000000000019884624838656,
"model_specific_special_tokens": {
"audio_token": "<|audio|>",
"boa_token": "<|audio>",
"boi_token": "<|image>",
"eoa_token": "<audio|>",
"eoc_token": "<channel|>",
"eoi_token": "<image|>",
"eot_token": "<turn|>",
"escape_token": "<|\"|>",
"etc_token": "<tool_call|>",
"etd_token": "<tool|>",
"etr_token": "<tool_response|>",
"image_token": "<|image|>",
"soc_token": "<|channel>",
"sot_token": "<|turn>",
"stc_token": "<|tool_call>",
"std_token": "<|tool>",
"str_token": "<|tool_response>",
"think_token": "<|think|>"
},
"pad_token": "<pad>",
"padding_side": "left",
"processor_class": "Gemma4Processor",
"soc_token": "<|channel>",
"sot_token": "<|turn>",
"stc_token": "<|tool_call>",
"std_token": "<|tool>",
"str_token": "<|tool_response>",
"think_token": "<|think|>",
"tokenizer_class": "GemmaTokenizer",
"unk_token": "<unk>"
}
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