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 Settings

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
```

[UNUSEDTOKEN145]

#15

by MoonRide - opened May 16, 2024

Discussion

MoonRide

May 16, 2024

•

edited May 17, 2024

Still something wrong with tokenizer (or the config). Reproduction steps below, using current version (b2901) of llama.cpp.

Steps:

Convert to GGUF: convert-hf-to-gguf.py --outtype f16 ..\InternLM2-Chat-7B\ --outfile InternLM2-Chat-7B-F16.gguf.
Quantize to Q6_K: quantize.exe .\InternLM2-Chat-7B-F16.gguf .\InternLM2-Chat-7B-Q6_K.gguf Q6_K.
Launch server: server -v -ngl 99 -m InternLM2-Chat-7B-Q6_K.gguf -n 300 -c 32768 --chat-template chatml
Open http://localhost:8080/, and setup it as below (pretty standard generic configuration):
Start talking:

Outcome: something wrong with tokenization, [UNUSEDTOKEN145] appears instead of end of turn.

Expected outcome: conversation in turns working properly, [UNUSEDTOKEN145] not appearing in conversation.

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