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

Libraries

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

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

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

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Use Docker

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

SGLang

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

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

请求适配vllm框架

by dafen - opened Jan 18, 2024

Discussion

dafen

Jan 18, 2024

模型效果很好，但是由于architectures变动，原来的vllm不能支持InternLM2ForCausalLM，请求适配。谢谢

x54-729

Jan 19, 2024

对比llama架构，internlm2主要是将Wq\Wk\Wv三个矩阵合并为了一个矩阵，这样可以让训练的速度增加5%，考虑到预训练的巨大消耗，这个优化对预训练非常重要。如果非常着急使用的话，可以尝试使用 https://github.com/InternLM/InternLM/tree/main/tools/convert2llama.py 脚本进行转换。

kiranr

Jan 19, 2024

•

edited Jan 19, 2024

I converted the model weights to llama format and uploaded it here https://huggingface.co/kiranr/internlm2-chat-20b-llama. hope it will be helpful.

Esmeetu

Intern Large Models org Jan 19, 2024

请问wqkv的组织形式和mpt模型的有什么不同吗？@x54-729

dafen

Jan 24, 2024

I converted the model weights to llama format and uploaded it here https://huggingface.co/kiranr/internlm2-chat-20b-llama. hope it will be helpful.

dafen changed discussion status to closed Jan 24, 2024

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