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)

# pip install -U transformers accelerate
# 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", device_map="auto")
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=256)
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-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 5, 2024

Commit

384e0ff

verified ·

1 Parent(s): c80d594

Update README.md

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -179,7 +179,7 @@ We conduct our evaluation along two dimensions:
 #### Multilingual Instruction-following Capability - SeaBench
 SeaBench consists of multi-turn human instructions spanning various task types. It evaluates chat-based models on their ability to follow human instructions in both single and multi-turn settings and assesses their performance across different task types. The dataset and corresponding evaluation code will be released soon!
-| model           |   id<br>turn-1 |   id<br>turn-2 |   id<br>avg |   th<br>turn-1 |   th<br>turn-2 |   th<br>avg |   vi<br>turn-1 |   vi<br>turn-2 |   vi<br>avg |   avg |
 |:----------------|------------:|------------:|---------:|------------:|------------:|---------:|------------:|------------:|---------:|------:|
 | Qwen2-7B-Instruct|         5.93 |         5.84 |     5.89 |         5.47 |         5.20 |     5.34 |         6.17 |         5.60 |     5.89 |  5.70 |
 | SeaLLM-7B-v2.5  |         6.27 |         4.96 |     5.62 |         5.79 |         3.82 |     4.81 |         6.02 |         4.02 |     5.02 |  5.15 |

 #### Multilingual Instruction-following Capability - SeaBench
 SeaBench consists of multi-turn human instructions spanning various task types. It evaluates chat-based models on their ability to follow human instructions in both single and multi-turn settings and assesses their performance across different task types. The dataset and corresponding evaluation code will be released soon!
+| model           |   id<br>turn1 |   id<br>turn2 |   id<br>avg |   th<br>turn1 |   th<br>turn2 |   th<br>avg |   vi<br>turn1 |   vi<br>turn2 |   vi<br>avg |   avg |
 |:----------------|------------:|------------:|---------:|------------:|------------:|---------:|------------:|------------:|---------:|------:|
 | Qwen2-7B-Instruct|         5.93 |         5.84 |     5.89 |         5.47 |         5.20 |     5.34 |         6.17 |         5.60 |     5.89 |  5.70 |
 | SeaLLM-7B-v2.5  |         6.27 |         4.96 |     5.62 |         5.79 |         3.82 |     4.81 |         6.02 |         4.02 |     5.02 |  5.15 |