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

Libraries

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

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

pipe = pipeline("text-generation", model="SeaLLMs/SeaLLMs-v3-7B-Chat")
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-Chat")
model = AutoModelForCausalLM.from_pretrained("SeaLLMs/SeaLLMs-v3-7B-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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Use Docker

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

SGLang

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

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

isakzhang commited on Jul 18, 2024

Commit

67ef6df

verified ·

1 Parent(s): 778f350

Update README.md

Browse files

Files changed (1) hide show

README.md +8 -6

README.md CHANGED Viewed

@@ -20,7 +20,7 @@ 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/SeaLLM3-7B-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;
@@ -40,7 +40,9 @@ 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-Chat model, specifically fine-tuned to follow human instructions effectively for task completion, making it directly applicable to your applications.
 ### Get started with `Transformers`
@@ -53,11 +55,11 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 device = "cuda" # the device to load the model onto
 model = AutoModelForCausalLM.from_pretrained(
-  "SeaLLMs/SeaLLM3-7B-chat",
   torch_dtype=torch.bfloat16,
   device_map=device
 )
-tokenizer = AutoTokenizer.from_pretrained("SeaLLMs/SeaLLM3-7B-chat")
 # prepare messages to model
 prompt = "Hiii How are you?"
@@ -89,11 +91,11 @@ from transformers import TextStreamer
 device = "cuda" # the device to load the model onto
 model = AutoModelForCausalLM.from_pretrained(
-  "SeaLLMs/SeaLLM3-7B-chat",
   torch_dtype=torch.bfloat16,
   device_map=device
 )
-tokenizer = AutoTokenizer.from_pretrained("SeaLLMs/SeaLLM3-7B-chat")
 # prepare messages to model
 messages = [

 <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-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;
 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-Chat** model, specifically fine-tuned to follow human instructions effectively for task completion, making it directly applicable to your applications.
+You may also refer to the [SeaLLMs-v3-1.5B-Chat](https://huggingface.co/SeaLLMs/SeaLLMs-v3-1.5B-Chat) model which requires much lower computational resources and can be easily loaded locally.
 ### Get started with `Transformers`
 device = "cuda" # the device to load the model onto
 model = AutoModelForCausalLM.from_pretrained(
+  "SeaLLMs/SeaLLMs-v3-7B-Chat", # can change to "SeaLLMs/SeaLLMs-v3-1.5B-Chat" if your resource is limited
   torch_dtype=torch.bfloat16,
   device_map=device
 )
+tokenizer = AutoTokenizer.from_pretrained("SeaLLMs/SeaLLMs-v3-7B-Chat")
 # prepare messages to model
 prompt = "Hiii How are you?"
 device = "cuda" # the device to load the model onto
 model = AutoModelForCausalLM.from_pretrained(
+  "SeaLLMs/SeaLLMs-v3-7B-Chat",  # can change to "SeaLLMs/SeaLLMs-v3-1.5B-Chat" if your resource is limited
   torch_dtype=torch.bfloat16,
   device_map=device
 )
+tokenizer = AutoTokenizer.from_pretrained("SeaLLMs/SeaLLMs-v3-7B-Chat")
 # prepare messages to model
 messages = [