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

Libraries

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

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

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Install from pip and serve model

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

Use Docker

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

SGLang

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

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

question for the pr of [update additional_special_tokens (#8)]

#10

by Qingyun - opened Mar 5, 2024

Discussion

Qingyun

Mar 5, 2024

•

edited Mar 5, 2024

This pr added additional_special_tokens, which seems result in mismatch of tokenizer length and vocablary size in my transformers==4.31.0 version.

  "additional_special_tokens": [
    "<|im_start|>",
    "<|im_end|>",
    "<|action_start|>",
    "<|action_end|>",
    "<|interpreter|>",
    "<|plugin|>"
  ],

tokenizer
ipdb> InternLM2Tokenizer(name_or_path='internlm/internlm2-chat-7b', vocab_size=92544, model_max_length=1000000000000000019884624838656, is_fast=False, padding_side='right', truncation_side='right', special_tokens={'bos_token': '<s>', 'eos_token': '</s>', 'unk_token': '<unk>', 'pad_token': '</s>', 'additional_special_tokens': ['<|im_start|>', '<|im_end|>', '<|action_start|>', '<|action_end|>', '<|interpreter|>', '<|plugin|>']}, clean_up_tokenization_spaces=False)
len(tokenizer)
ipdb> 92550

It seems that the additional special tokens are made new ids, which is mismatched with the input_embeddings. But this pr seems to resolve the bug in 4.33.2 as described in this issue.

Qingyun changed discussion status to closed Mar 20, 2024

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