Instructions to use aisingapore/SEA-LION-v1-7B-IT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aisingapore/SEA-LION-v1-7B-IT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="aisingapore/SEA-LION-v1-7B-IT", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("aisingapore/SEA-LION-v1-7B-IT", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("aisingapore/SEA-LION-v1-7B-IT", trust_remote_code=True) 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 Settings
- vLLM
How to use aisingapore/SEA-LION-v1-7B-IT with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "aisingapore/SEA-LION-v1-7B-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": "aisingapore/SEA-LION-v1-7B-IT", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/aisingapore/SEA-LION-v1-7B-IT
- SGLang
How to use aisingapore/SEA-LION-v1-7B-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 "aisingapore/SEA-LION-v1-7B-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": "aisingapore/SEA-LION-v1-7B-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 "aisingapore/SEA-LION-v1-7B-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": "aisingapore/SEA-LION-v1-7B-IT", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use aisingapore/SEA-LION-v1-7B-IT with Docker Model Runner:
docker model run hf.co/aisingapore/SEA-LION-v1-7B-IT
Commit History
Add chat template in tokenizer_config.json b667b1c verified
Update metrics in README.md 566afff verified
Fix the bug in generation config (#2) cab651c verified
Update tokenizer.model for GGUF quantization 5c84557 verified
Update README.md 07d2e1f verified
Update README.md 68b25b8 verified
Update README.md a7bb812 verified
Updated readme to add more details on bhasa 5282321 verified
update links to new naming scheme a28abe3 verified
Update instruct model to latest weights 881b143
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