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

Libraries

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

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

pipe = pipeline("text-generation", model="internlm/internlm2-math-base-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-math-base-20b", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use internlm/internlm2-math-base-20b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "internlm/internlm2-math-base-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-math-base-20b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

InternLM-Math commited on Jan 23, 2024

Commit

294abec

verified ·

1 Parent(s): f491935

Update configuration_internlm.py

Browse files

Files changed (1) hide show

configuration_internlm.py +5 -0

configuration_internlm.py CHANGED Viewed

@@ -108,6 +108,7 @@ class InternLMConfig(PretrainedConfig):
         bias=True,
         rope_theta=10000,
         rope_scaling=None,
         **kwargs,
     ):
         self.vocab_size = vocab_size
@@ -129,6 +130,10 @@ class InternLMConfig(PretrainedConfig):
         self.rope_theta = rope_theta
         self.rope_scaling = rope_scaling
         self._rope_scaling_validation()
         super().__init__(
             pad_token_id=pad_token_id,
             bos_token_id=bos_token_id,

         bias=True,
         rope_theta=10000,
         rope_scaling=None,
+        attn_implementation="eager",
         **kwargs,
     ):
         self.vocab_size = vocab_size
         self.rope_theta = rope_theta
         self.rope_scaling = rope_scaling
         self._rope_scaling_validation()
+        self.attn_implementation = attn_implementation
+        if self.attn_implementation is None:
+            self.attn_implementation = "eager"
         super().__init__(
             pad_token_id=pad_token_id,
             bos_token_id=bos_token_id,