Instructions to use internlm/internlm2-chat-20b-4bits with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use internlm/internlm2-chat-20b-4bits with Transformers:

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

pipe = pipeline("text-generation", model="internlm/internlm2-chat-20b-4bits", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("internlm/internlm2-chat-20b-4bits", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use internlm/internlm2-chat-20b-4bits with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "internlm/internlm2-chat-20b-4bits"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "internlm/internlm2-chat-20b-4bits",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/internlm/internlm2-chat-20b-4bits

SGLang

How to use internlm/internlm2-chat-20b-4bits 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 "internlm/internlm2-chat-20b-4bits" \
    --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": "internlm/internlm2-chat-20b-4bits",
		"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 "internlm/internlm2-chat-20b-4bits" \
        --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": "internlm/internlm2-chat-20b-4bits",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use internlm/internlm2-chat-20b-4bits with Docker Model Runner:
```
docker model run hf.co/internlm/internlm2-chat-20b-4bits
```

whcao commited on Jan 16, 2024

Commit

3db11ab

1 Parent(s): a059820

fix readme

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -40,7 +40,7 @@ Trying the following codes, you can perform the batched offline inference with t
 ```python
 from lmdeploy import pipeline, TurbomindEngineConfig
 engine_config = TurbomindEngineConfig(model_format='awq')
-pipe = pipeline("internlm/internlm2-chat-20b-4bits", engine_config)
 response = pipe(["Hi, pls intro yourself", "Shanghai is"])
 print(response)
 ```

 ```python
 from lmdeploy import pipeline, TurbomindEngineConfig
 engine_config = TurbomindEngineConfig(model_format='awq')
+pipe = pipeline("internlm/internlm2-chat-20b-4bits", backend_config=engine_config)
 response = pipe(["Hi, pls intro yourself", "Shanghai is"])
 print(response)
 ```