Instructions to use supalun/gemma2_ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use supalun/gemma2_ft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="supalun/gemma2_ft")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("supalun/gemma2_ft") model = AutoModelForCausalLM.from_pretrained("supalun/gemma2_ft", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use supalun/gemma2_ft with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "supalun/gemma2_ft" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "supalun/gemma2_ft", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/supalun/gemma2_ft
- SGLang
How to use supalun/gemma2_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 "supalun/gemma2_ft" \ --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": "supalun/gemma2_ft", "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 "supalun/gemma2_ft" \ --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": "supalun/gemma2_ft", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use supalun/gemma2_ft with Docker Model Runner:
docker model run hf.co/supalun/gemma2_ft
Download tokenizer.json from supalun/gemma2_ft: direct link, hf CLI and curl.
- Browser
- Download file 34.4 MB
-
https://huggingface.co/supalun/gemma2_ft/resolve/main/tokenizer.json
- Command line
-
hf download hf://supalun/gemma2_ft/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/supalun/gemma2_ft/resolve/main/tokenizer.json
34.4 MB
- Xet hash:
- 09910d185298ea698cce2078db489cb9f86948e69514d6f90952c8890479687b
- Size of remote file:
- 34.4 MB
- SHA256:
- e7a4a8c2794b64716e1ae631549f880777a169af6a393638f691da53bc72e6ae
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