Text Generation
Transformers
Safetensors
qwen2
multilingual
sea
sailor
sft
chat
instruction
conversational
text-generation-inference
Instructions to use sail/Sailor2-8B-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sail/Sailor2-8B-Chat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="sail/Sailor2-8B-Chat") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("sail/Sailor2-8B-Chat") model = AutoModelForCausalLM.from_pretrained("sail/Sailor2-8B-Chat") 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 sail/Sailor2-8B-Chat with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sail/Sailor2-8B-Chat" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sail/Sailor2-8B-Chat", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/sail/Sailor2-8B-Chat
- SGLang
How to use sail/Sailor2-8B-Chat 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 "sail/Sailor2-8B-Chat" \ --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": "sail/Sailor2-8B-Chat", "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 "sail/Sailor2-8B-Chat" \ --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": "sail/Sailor2-8B-Chat", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use sail/Sailor2-8B-Chat with Docker Model Runner:
docker model run hf.co/sail/Sailor2-8B-Chat
Add pipeline tag, library name
#2
by nielsr HF Staff - opened
README.md
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---
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language:
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- en
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- km
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tags:
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- multilingual
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- sea
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- sft
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- chat
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- instruction
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widget:
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- text: 如何制作烤鱼?
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example_title: Chinese
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example_title: Indonesian
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- text: Làm thế nào để nướng cá?
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example_title: Vietnamese
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license: apache-2.0
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base_model:
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---
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<div align="center">
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<img src="sailor2_banner.jpg" width="700"/>
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</div>
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# Contact Us
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If you have any questions, please raise an issue or contact us at [doulx@sea.com](mailto:doulx@sea.com) or [liuqian.sea@gmail.com](mailto:liuqian.sea@gmail.com).
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base_model:
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language:
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- en
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- zh
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- km
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- su
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license: apache-2.0
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library_name: transformers
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tags:
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- multilingual
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- sea
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- sft
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- chat
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- instruction
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pipeline_tag: text-generation
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widget:
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- text: 如何制作烤鱼?
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example_title: Chinese
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example_title: Indonesian
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- text: Làm thế nào để nướng cá?
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example_title: Vietnamese
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---
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```markdown
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<div align="center">
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<img src="sailor2_banner.jpg" width="700"/>
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</div>
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# Contact Us
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If you have any questions, please raise an issue or contact us at [doulx@sea.com](mailto:doulx@sea.com) or [liuqian.sea@gmail.com](mailto:liuqian.sea@gmail.com).
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```
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