Instructions to use internlm/internlm2-math-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use internlm/internlm2-math-7b with Transformers:

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

pipe = pipeline("text-generation", model="internlm/internlm2-math-7b", 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-7b", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use internlm/internlm2-math-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "internlm/internlm2-math-7b"
# 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-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/internlm/internlm2-math-7b

SGLang

How to use internlm/internlm2-math-7b 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-7b" \
    --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-7b",
		"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-7b" \
        --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-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use internlm/internlm2-math-7b with Docker Model Runner:
```
docker model run hf.co/internlm/internlm2-math-7b
```

x54-729 commited on Jan 29, 2024

Commit

6f4ea21

1 Parent(s): 32b8973

update readme

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@@ -121,18 +121,23 @@ print(response)
 We list some instructions used in our SFT. You can use them to help you. You can use the other ways to prompt the model, but the following are recommended. InternLM2-Math may combine the following abilities but it is not guaranteed.
 Translate proof problem to Lean:
 ![nl2lean3](https://raw.githubusercontent.com/InternLM/InternLM-Math/main/assets/nl2lean.jpeg)
 Using Lean 3 to solve GSM8K problem:
 ![gsm8k_lean](https://raw.githubusercontent.com/InternLM/InternLM-Math/main/assets/gsm8k_lean.jpeg)
 Generate problem based on Lean 3 code:
 ![lean_problem](https://raw.githubusercontent.com/InternLM/InternLM-Math/main/assets/lean_problem.jpeg)
 Play 24 point game:
 ![24](https://raw.githubusercontent.com/InternLM/InternLM-Math/main/assets/24.jpeg)
 Augment a harder math problem:
 ![augment_hard](https://raw.githubusercontent.com/InternLM/InternLM-Math/main/assets/augment_hard.jpeg)
 | Description | Query |

 We list some instructions used in our SFT. You can use them to help you. You can use the other ways to prompt the model, but the following are recommended. InternLM2-Math may combine the following abilities but it is not guaranteed.
 Translate proof problem to Lean:
 ![nl2lean3](https://raw.githubusercontent.com/InternLM/InternLM-Math/main/assets/nl2lean.jpeg)
 Using Lean 3 to solve GSM8K problem:
 ![gsm8k_lean](https://raw.githubusercontent.com/InternLM/InternLM-Math/main/assets/gsm8k_lean.jpeg)
 Generate problem based on Lean 3 code:
 ![lean_problem](https://raw.githubusercontent.com/InternLM/InternLM-Math/main/assets/lean_problem.jpeg)
 Play 24 point game:
 ![24](https://raw.githubusercontent.com/InternLM/InternLM-Math/main/assets/24.jpeg)
 Augment a harder math problem:
 ![augment_hard](https://raw.githubusercontent.com/InternLM/InternLM-Math/main/assets/augment_hard.jpeg)
 | Description | Query |