Instructions to use lunahr/SystemGemma2-2b-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lunahr/SystemGemma2-2b-it with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="lunahr/SystemGemma2-2b-it") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("lunahr/SystemGemma2-2b-it") model = AutoModelForCausalLM.from_pretrained("lunahr/SystemGemma2-2b-it") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use lunahr/SystemGemma2-2b-it with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lunahr/SystemGemma2-2b-it" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lunahr/SystemGemma2-2b-it", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/lunahr/SystemGemma2-2b-it
- SGLang
How to use lunahr/SystemGemma2-2b-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 "lunahr/SystemGemma2-2b-it" \ --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": "lunahr/SystemGemma2-2b-it", "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 "lunahr/SystemGemma2-2b-it" \ --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": "lunahr/SystemGemma2-2b-it", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use lunahr/SystemGemma2-2b-it with Docker Model Runner:
docker model run hf.co/lunahr/SystemGemma2-2b-it
Tokenizer not downloading correctly with LFS
I have Git LFS and tokenizer.json won't download. This is what's in the file:
version https://git-lfs.github.com/spec/v1
oid sha256:3f289bc05132635a8bc7aca7aa21255efd5e18f3710f43e3cdb96bcd41be4922
size 17525357
The other LFS files downloaded correctly
since the .gitattributes are incorrect, i need to update them to allow tokenizer.json to be downloaded.
the SystemGemmas will be updated
I replaced the template in tokenizer_conig.json of the original model with the one here https://huggingface.co/google/gemma-2-2b-it/discussions/25, and now my system prompt generates twice:
conversations = [
[
{"role": "system", "content": "This is a system prompt"},
{"role": "user", "content": "This is some User Text"}
]
for doc in docs
]
prompts = tokenizer.apply_chat_template(conversations, tokenize=False, add_generation_prompt=True)
print(prompts[0])
<bos>This is a system prompt
<start_of_turn>system
This is a system prompt<end_of_turn>
<start_of_turn>user
This is some User Text<end_of_turn>
<start_of_turn>model
I replaced the template in tokenizer_conig.json of the original model with the one here https://huggingface.co/google/gemma-2-2b-it/discussions/25, and now my system prompt generates twice:
conversations = [ [ {"role": "system", "content": "This is a system prompt"}, {"role": "user", "content": "This is some User Text"} ] for doc in docs ] prompts = tokenizer.apply_chat_template(conversations, tokenize=False, add_generation_prompt=True) print(prompts[0])<bos>This is a system prompt <start_of_turn>system This is a system prompt<end_of_turn> <start_of_turn>user This is some User Text<end_of_turn> <start_of_turn>model
Sounds like the template is parsing system prompt in the beginning of each turn, so I have to disable appending of system prompt in the turns.
Thanks!