Instructions to use google/gemma-3-4b-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/gemma-3-4b-it with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="google/gemma-3-4b-it")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("google/gemma-3-4b-it", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use google/gemma-3-4b-it with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "google/gemma-3-4b-it" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "google/gemma-3-4b-it", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/google/gemma-3-4b-it
- SGLang
How to use google/gemma-3-4b-it 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 "google/gemma-3-4b-it" \ --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": "google/gemma-3-4b-it", "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 "google/gemma-3-4b-it" \ --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": "google/gemma-3-4b-it", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use google/gemma-3-4b-it with Docker Model Runner:
docker model run hf.co/google/gemma-3-4b-it
Token Count Calculation in SFT Data Distribution Curation
Regarding the curation of SFT data, including the curation of the data distribution, I would like to understand how you calculate the token count for each data entry when designing the distribution. Is the token count based only on the user tokens, or does it also include the assistant tokens? (The reason I ask is that I understand the SFT loss is calculated only based on the assistant tokens.)
Hi @tcy006 ,
The token count isn't one number. Both counts are calculated and used. When considering training cost and efficiency, token count usually refers to the total tokens. When considering task balancing and model behavior, it refers to the assistant tokens.
- Assistant-Only Tokens: This metric measures the length of the assistant's response (len(assistant_response)). Its primary purpose is for learning & task balancing answering the question.
- Total Tokens: This metric measures the combined length of the user prompt and the assistant's response (len(user_prompt) + len(assistant_response)). Its primary purpose is for managing infrastructure & cost answering the question.
Thanks.
Hi @tcy006 ,
The token count isn't one number. Both counts are calculated and used. When considering training cost and efficiency, token count usually refers to the total tokens. When considering task balancing and model behavior, it refers to the assistant tokens.
- Assistant-Only Tokens: This metric measures the length of the assistant's response (len(assistant_response)). Its primary purpose is for learning & task balancing answering the question.
- Total Tokens: This metric measures the combined length of the user prompt and the assistant's response (len(user_prompt) + len(assistant_response)). Its primary purpose is for managing infrastructure & cost answering the question.
Thanks.
Hi @BalakrishnaCh ,
Thank you for the clear explanation — it really helps clarify how both token counts play different roles in SFT data curation.
I appreciate you breaking down the distinction between assistant-only tokens and total tokens, and how each is used for task balancing versus training cost considerations. This makes a lot of sense.
Thanks again for the detailed and helpful response!