Instructions to use typealias/internlm2_5-7b-chat-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use typealias/internlm2_5-7b-chat-4bit with Transformers:

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

pipe = pipeline("text-generation", model="typealias/internlm2_5-7b-chat-4bit", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("typealias/internlm2_5-7b-chat-4bit", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use typealias/internlm2_5-7b-chat-4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "typealias/internlm2_5-7b-chat-4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "typealias/internlm2_5-7b-chat-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/typealias/internlm2_5-7b-chat-4bit

SGLang

How to use typealias/internlm2_5-7b-chat-4bit 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 "typealias/internlm2_5-7b-chat-4bit" \
    --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": "typealias/internlm2_5-7b-chat-4bit",
		"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 "typealias/internlm2_5-7b-chat-4bit" \
        --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": "typealias/internlm2_5-7b-chat-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use typealias/internlm2_5-7b-chat-4bit with Docker Model Runner:
```
docker model run hf.co/typealias/internlm2_5-7b-chat-4bit
```

INT4 Weight-only Quantization and Deployment (W4A16)

LMDeploy adopts AWQ algorithm for 4bit weight-only quantization. By developed the high-performance cuda kernel, the 4bit quantized model inference achieves up to 2.4x faster than FP16.

LMDeploy supports the following NVIDIA GPU for W4A16 inference:

Turing(sm75): 20 series, T4
Ampere(sm80,sm86): 30 series, A10, A16, A30, A100
Ada Lovelace(sm90): 40 series

Before proceeding with the quantization and inference, please ensure that lmdeploy is installed.

pip install lmdeploy[all]

This article comprises the following sections:

Inference
Evaluation
Service

Inference

Trying the following codes, you can perform the batched offline inference with the quantized model:

from lmdeploy import pipeline, TurbomindEngineConfig
engine_config = TurbomindEngineConfig(model_format='awq')
pipe = pipeline("internlm/internlm2_5-7b-chat-4bit", backend_config=engine_config)
response = pipe(["Hi, pls intro yourself", "Shanghai is"])
print(response)

For more information about the pipeline parameters, please refer to here.

Evaluation

Please overview this guide about model evaluation with LMDeploy.

Service

LMDeploy's api_server enables models to be easily packed into services with a single command. The provided RESTful APIs are compatible with OpenAI's interfaces. Below are an example of service startup:

lmdeploy serve api_server internlm/internlm2_5-7b-chat-4bit --backend turbomind --model-format awq

The default port of api_server is 23333. After the server is launched, you can communicate with server on terminal through api_client:

lmdeploy serve api_client http://0.0.0.0:23333

You can overview and try out api_server APIs online by swagger UI at http://0.0.0.0:23333, or you can also read the API specification from here.

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Safetensors

Model size

8B params

Tensor type

F16

Paper for typealias/internlm2_5-7b-chat-4bit

AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Paper • 2306.00978 • Published Jun 1, 2023 • 12