Instructions to use internlm/internlm-xcomposer2-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use internlm/internlm-xcomposer2-7b with Transformers:

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

pipe = pipeline("text-generation", model="internlm/internlm-xcomposer2-7b", trust_remote_code=True)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("internlm/internlm-xcomposer2-7b", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use internlm/internlm-xcomposer2-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "internlm/internlm-xcomposer2-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "internlm/internlm-xcomposer2-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/internlm/internlm-xcomposer2-7b

SGLang

How to use internlm/internlm-xcomposer2-7b 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/internlm-xcomposer2-7b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "internlm/internlm-xcomposer2-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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/internlm-xcomposer2-7b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "internlm/internlm-xcomposer2-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use internlm/internlm-xcomposer2-7b with Docker Model Runner:
```
docker model run hf.co/internlm/internlm-xcomposer2-7b
```

fix(internlm): Prevent errors by padding the dimensions of wrap tokens.

by yun - opened Mar 11, 2024

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

-9

yun

Mar 11, 2024

The text_input in a batch can contain texts of various lengths.
In this case, the wrap_tokens will be of different lengths and torch.cat will get an error because the dim is not correct.
I added padding to resolve the issue below.
I would appreciate it if you could review this PR.
Error examples

ret_val = func(*args, **kwargs)
File "/tmp/ray/session_2024-02-05_14-30-33_881744_3780/runtime_resources/pip/40ae4806a7327971d7c077068c4b0a3019a14611/virtualenv/lib/python3.10/site-packages/deepspeed/runtime/engine.py", line 1801, in forward
loss = self.module(*inputs, **kwargs)
File "/opt/conda/envs/py310/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/opt/conda/envs/py310/lib/python3.10/site-packages/pytorch_lightning/overrides/base.py", line 98, in forward
output = self._forward_module.training_step(*inputs, **kwargs)
File "/tmp/ray/session_2024-02-05_14-30-33_881744_3780/runtime_resources/py_modules_files/_ray_pkg_33670290aabc83b3/ml/model/application/vlm/place_vlm/llava/system_stage2_internlm.py", line 56, in training_step
outputs = self.model(samples=batch)
File "/opt/conda/envs/py310/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/root/.cache/huggingface/modules/transformers_modules/internlm2-7b/modeling_internlm_xcomposer2.py", line 337, in forward
to_regress_embeds, attention_mask, targets, im_mask = self.interleav_wrap(
File "/root/.cache/huggingface/modules/transformers_modules/internlm2-7b/modeling_internlm_xcomposer2.py", line 266, in interleav_wrap
wrap_embeds = torch.cat(wrap_embeds_list)
RuntimeError: Sizes of tensors must match except in dimension 0. Expected size 1230 but got size 3546 for tensor number 1 in the list.

fix(internlm): Prevent errors by padding the dimensions of wrap tokens.de2ef9d8

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