Instructions to use internlm/internlm2-math-plus-1_8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use internlm/internlm2-math-plus-1_8b with Transformers:

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

pipe = pipeline("text-generation", model="internlm/internlm2-math-plus-1_8b", 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-math-plus-1_8b", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use internlm/internlm2-math-plus-1_8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "internlm/internlm2-math-plus-1_8b"
# 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-math-plus-1_8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/internlm/internlm2-math-plus-1_8b

SGLang

How to use internlm/internlm2-math-plus-1_8b 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-math-plus-1_8b" \
    --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-math-plus-1_8b",
		"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-math-plus-1_8b" \
        --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-math-plus-1_8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use internlm/internlm2-math-plus-1_8b with Docker Model Runner:
```
docker model run hf.co/internlm/internlm2-math-plus-1_8b
```

InternLM-Math commited on May 24, 2024

Commit

a1ce887

verified ·

1 Parent(s): 3aec4ee

Update README.md

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README.md +1 -1

README.md CHANGED Viewed

@@ -88,7 +88,7 @@ We also evaluate models on [MathBench-A](https://github.com/open-compass/MathBen
 | Deepseek-Math-7B-RL              | 68.0       | 83.3    | 44.3   | 33.0 | 23.0    | 50.3    |
 | InternLM2-Math-Plus-7B           | 61.4       | 78.3    | 52.5   | 40.5 | 21.7    | 50.9    |
 | MiniCPM-2B                       | 49.3       | 51.7    | 18.0   | 8.7  | 3.7     | 26.3    |
-| InternLM2-Math-Plus-1.8B         | 43.0       | 43.0    | 43.3   | 25.4 | 4.7     | 27.1    |
 # Citation and Tech Report
 ```

 | Deepseek-Math-7B-RL              | 68.0       | 83.3    | 44.3   | 33.0 | 23.0    | 50.3    |
 | InternLM2-Math-Plus-7B           | 61.4       | 78.3    | 52.5   | 40.5 | 21.7    | 50.9    |
 | MiniCPM-2B                       | 49.3       | 51.7    | 18.0   | 8.7  | 3.7     | 26.3    |
+| InternLM2-Math-Plus-1.8B         | 43.0       | 43.3    | 25.4   | 18.9 | 4.7     | 27.1    |
 # Citation and Tech Report
 ```