Instructions to use yashpratap/gemma3n-e4b-it-generic-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use yashpratap/gemma3n-e4b-it-generic-adapter with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-3n-E4B-it") model = PeftModel.from_pretrained(base_model, "yashpratap/gemma3n-e4b-it-generic-adapter") - Transformers
How to use yashpratap/gemma3n-e4b-it-generic-adapter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="yashpratap/gemma3n-e4b-it-generic-adapter") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("yashpratap/gemma3n-e4b-it-generic-adapter", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use yashpratap/gemma3n-e4b-it-generic-adapter with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "yashpratap/gemma3n-e4b-it-generic-adapter" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "yashpratap/gemma3n-e4b-it-generic-adapter", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/yashpratap/gemma3n-e4b-it-generic-adapter
- SGLang
How to use yashpratap/gemma3n-e4b-it-generic-adapter 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 "yashpratap/gemma3n-e4b-it-generic-adapter" \ --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": "yashpratap/gemma3n-e4b-it-generic-adapter", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "yashpratap/gemma3n-e4b-it-generic-adapter" \ --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": "yashpratap/gemma3n-e4b-it-generic-adapter", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Desktop
- Docker Model Runner
How to use yashpratap/gemma3n-e4b-it-generic-adapter with Docker Model Runner:
docker model run hf.co/yashpratap/gemma3n-e4b-it-generic-adapter
Download preprocessor_config.json from yashpratap/gemma3n-e4b-it-generic-adapter: direct link, hf CLI and curl.
- Browser
- Download file 1.25 kB
-
https://huggingface.co/yashpratap/gemma3n-e4b-it-generic-adapter/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://yashpratap/gemma3n-e4b-it-generic-adapter/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/yashpratap/gemma3n-e4b-it-generic-adapter/resolve/main/preprocessor_config.json
1.25 kB
| { | |
| "crop_size": null, | |
| "data_format": "channels_first", | |
| "default_to_square": false, | |
| "device": null, | |
| "disable_grouping": null, | |
| "dither": 0.0, | |
| "do_center_crop": null, | |
| "do_convert_rgb": null, | |
| "do_normalize": false, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "feature_extractor_type": "Gemma3nAudioFeatureExtractor", | |
| "feature_size": 128, | |
| "fft_length": 1024, | |
| "fft_overdrive": true, | |
| "frame_length": 512, | |
| "hop_length": 160, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "SiglipImageProcessorFast", | |
| "image_seq_length": 256, | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "input_data_format": null, | |
| "input_scale_factor": 1.0, | |
| "max_frequency": 7600.0, | |
| "mel_floor": 1e-05, | |
| "min_frequency": 125.0, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "per_bin_mean": null, | |
| "per_bin_stddev": null, | |
| "preemphasis": 0.97, | |
| "preemphasis_htk_flavor": true, | |
| "processor_class": "Gemma3nProcessor", | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "return_attention_mask": true, | |
| "return_tensors": null, | |
| "sampling_rate": 16000, | |
| "size": { | |
| "height": 768, | |
| "width": 768 | |
| } | |
| } |