Instructions to use SeaLLMs/SeaLLMs-v3-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SeaLLMs/SeaLLMs-v3-7B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="SeaLLMs/SeaLLMs-v3-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SeaLLMs/SeaLLMs-v3-7B")
model = AutoModelForCausalLM.from_pretrained("SeaLLMs/SeaLLMs-v3-7B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SeaLLMs/SeaLLMs-v3-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SeaLLMs/SeaLLMs-v3-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SeaLLMs/SeaLLMs-v3-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SeaLLMs/SeaLLMs-v3-7B

SGLang

How to use SeaLLMs/SeaLLMs-v3-7B 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 "SeaLLMs/SeaLLMs-v3-7B" \
    --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": "SeaLLMs/SeaLLMs-v3-7B",
		"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 "SeaLLMs/SeaLLMs-v3-7B" \
        --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": "SeaLLMs/SeaLLMs-v3-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SeaLLMs/SeaLLMs-v3-7B with Docker Model Runner:
```
docker model run hf.co/SeaLLMs/SeaLLMs-v3-7B
```

isakzhang commited on Jul 30, 2024

Commit

7b039e4

verified ·

1 Parent(s): 9ae66db

Update README.md

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README.md +5 -3

README.md CHANGED Viewed

@@ -20,13 +20,13 @@ tags:
 <p align="center">
 <a href="https://damo-nlp-sg.github.io/SeaLLMs/" target="_blank" rel="noopener">Website</a>
 &nbsp;&nbsp;
-<a href="https://huggingface.co/SeaLLMs/SeaLLMs-v3-1.5B-Chat" target="_blank" rel="noopener"> 🤗 Tech Memo</a>
 &nbsp;&nbsp;
 <a href="https://huggingface.co/spaces/SeaLLMs/SeaLLM-Chat" target="_blank" rel="noopener"> 🤗 DEMO</a>
 &nbsp;&nbsp;
 <a href="https://github.com/DAMO-NLP-SG/SeaLLMs" target="_blank" rel="noopener">Github</a>
 &nbsp;&nbsp;
-<a href="https://arxiv.org/pdf/2407.19672" target="_blank" rel="noopener">Technical Report</a>
 </p>
@@ -42,7 +42,8 @@ We introduce **SeaLLMs-v3**, the latest series of the SeaLLMs (Large Language Mo
 SeaLLMs is tailored for handling a wide range of languages spoken in the SEA region, including English, Chinese, Indonesian, Vietnamese, Thai, Tagalog, Malay, Burmese, Khmer, Lao, Tamil, and Javanese.
-This page introduces the **SeaLLMs-v3-7B** model, which can be easily fine-tuned for your specific downstream tasks, especially in SEA languages.
 ## Evaluation
@@ -110,6 +111,7 @@ If you find our project useful, we hope you would kindly star our repo and cite
             Yew Ken Chia, Xin Li, Lidong Bing},
   title = {SeaLLMs 3: Open Foundation and Chat Multilingual Large Language Models for Southeast Asian Languages},
   year = {2024},
 }
 ```

 <p align="center">
 <a href="https://damo-nlp-sg.github.io/SeaLLMs/" target="_blank" rel="noopener">Website</a>
 &nbsp;&nbsp;
+<a href="https://huggingface.co/SeaLLMs/SeaLLMs-v3-7B" target="_blank" rel="noopener">Model</a>
 &nbsp;&nbsp;
 <a href="https://huggingface.co/spaces/SeaLLMs/SeaLLM-Chat" target="_blank" rel="noopener"> 🤗 DEMO</a>
 &nbsp;&nbsp;
 <a href="https://github.com/DAMO-NLP-SG/SeaLLMs" target="_blank" rel="noopener">Github</a>
 &nbsp;&nbsp;
+<a href="https://arxiv.org/pdf/2407.19672" target="_blank" rel="noopener">[NEW] Technical Report</a>
 </p>
 SeaLLMs is tailored for handling a wide range of languages spoken in the SEA region, including English, Chinese, Indonesian, Vietnamese, Thai, Tagalog, Malay, Burmese, Khmer, Lao, Tamil, and Javanese.
+This page introduces the **SeaLLMs-v3-7B** model, which can be fine-tuned for your specific downstream tasks, especially in SEA languages.
+Note that this is a base model, if you are looking for a model that can be directly applicable to your downstream applications, you may want to check the chat version model: **[SeaLLMs-v3-7B-Chat](https://huggingface.co/SeaLLMs/SeaLLMs-v3-7B-Chat)**.
 ## Evaluation
             Yew Ken Chia, Xin Li, Lidong Bing},
   title = {SeaLLMs 3: Open Foundation and Chat Multilingual Large Language Models for Southeast Asian Languages},
   year = {2024},
+  url = {https://arxiv.org/abs/2407.19672}
 }
 ```