Instructions to use OpenGVLab/InternVL-Chat-V1-5-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenGVLab/InternVL-Chat-V1-5-AWQ with Transformers:

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

pipe = pipeline("image-text-to-text", model="OpenGVLab/InternVL-Chat-V1-5-AWQ", trust_remote_code=True)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("OpenGVLab/InternVL-Chat-V1-5-AWQ", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OpenGVLab/InternVL-Chat-V1-5-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenGVLab/InternVL-Chat-V1-5-AWQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenGVLab/InternVL-Chat-V1-5-AWQ",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/OpenGVLab/InternVL-Chat-V1-5-AWQ

SGLang

How to use OpenGVLab/InternVL-Chat-V1-5-AWQ 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 "OpenGVLab/InternVL-Chat-V1-5-AWQ" \
    --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": "OpenGVLab/InternVL-Chat-V1-5-AWQ",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "OpenGVLab/InternVL-Chat-V1-5-AWQ" \
        --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": "OpenGVLab/InternVL-Chat-V1-5-AWQ",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use OpenGVLab/InternVL-Chat-V1-5-AWQ with Docker Model Runner:
```
docker model run hf.co/OpenGVLab/InternVL-Chat-V1-5-AWQ
```

How to load without lmdeploy?

by matatonic - opened May 30, 2024

Discussion

matatonic

May 30, 2024

Is there an example for just transformers?

Thanks for this model, it's really great!

matatonic

May 30, 2024

The lmdeploy official docker is also failing to start this model "ModuleNotFoundError: No module named 'timm'"

czczup

OpenGVLab org May 30, 2024

Try to install a timm?

Like this:

pip install timm==0.9.12

matatonic

May 30, 2024

I did try that, I got new errors.

Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.                                                                                                                    
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
Traceback (most recent call last):
  File "/opt/py38/bin/lmdeploy", line 11, in <module>
    load_entry_point('lmdeploy', 'console_scripts', 'lmdeploy')()
  File "/opt/lmdeploy/lmdeploy/cli/entrypoint.py", line 37, in run
    args.run(args)
  File "/opt/lmdeploy/lmdeploy/cli/serve.py", line 303, in api_server
    run_api_server(args.model_path,
  File "/opt/lmdeploy/lmdeploy/serve/openai/api_server.py", line 1191, in serve
    VariableInterface.async_engine = pipeline_class(
  File "/opt/lmdeploy/lmdeploy/serve/vl_async_engine.py", line 21, in __init__
    super().__init__(model_path, **kwargs)
  File "/opt/lmdeploy/lmdeploy/serve/async_engine.py", line 206, in __init__
    self._build_turbomind(model_path=model_path,
  File "/opt/lmdeploy/lmdeploy/serve/async_engine.py", line 253, in _build_turbomind
    self.engine = tm.TurboMind.from_pretrained(
  File "/opt/lmdeploy/lmdeploy/turbomind/turbomind.py", line 387, in from_pretrained
    return cls(model_path=pretrained_model_name_or_path,
  File "/opt/lmdeploy/lmdeploy/turbomind/turbomind.py", line 161, in __init__
    self.model_comm = self._from_hf(model_source=model_source,
  File "/opt/lmdeploy/lmdeploy/turbomind/turbomind.py", line 270, in _from_hf
    output_model = OUTPUT_MODELS.get(output_format)(
  File "/opt/lmdeploy/lmdeploy/turbomind/deploy/target_model/fp.py", line 26, in __init__
    super().__init__(input_model, cfg, to_file, out_dir)
  File "/opt/lmdeploy/lmdeploy/turbomind/deploy/target_model/base.py", line 156, in __init__
    self.cfg = self.get_config(cfg)
  File "/opt/lmdeploy/lmdeploy/turbomind/deploy/target_model/fp.py", line 38, in get_config
    w1, _, _ = bin.ffn(i)
  File "/opt/lmdeploy/lmdeploy/turbomind/deploy/source_model/internlm2.py", line 69, in ffn
    return self._ffn(i, 'weight')
  File "/opt/lmdeploy/lmdeploy/turbomind/deploy/source_model/internlm2.py", line 62, in _ffn
    tensor = self.params[
KeyError: 'language_model.model.layers.0.feed_forward.w1.weight'

matatonic

May 30, 2024

I would still also prefer to run it directly without lmdeploy if possible, thanks!

zwgao

OpenGVLab org Aug 21, 2024

I did try that, I got new errors.

Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.                                                                                                                    
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
Traceback (most recent call last):
  File "/opt/py38/bin/lmdeploy", line 11, in <module>
    load_entry_point('lmdeploy', 'console_scripts', 'lmdeploy')()
  File "/opt/lmdeploy/lmdeploy/cli/entrypoint.py", line 37, in run
    args.run(args)
  File "/opt/lmdeploy/lmdeploy/cli/serve.py", line 303, in api_server
    run_api_server(args.model_path,
  File "/opt/lmdeploy/lmdeploy/serve/openai/api_server.py", line 1191, in serve
    VariableInterface.async_engine = pipeline_class(
  File "/opt/lmdeploy/lmdeploy/serve/vl_async_engine.py", line 21, in __init__
    super().__init__(model_path, **kwargs)
  File "/opt/lmdeploy/lmdeploy/serve/async_engine.py", line 206, in __init__
    self._build_turbomind(model_path=model_path,
  File "/opt/lmdeploy/lmdeploy/serve/async_engine.py", line 253, in _build_turbomind
    self.engine = tm.TurboMind.from_pretrained(
  File "/opt/lmdeploy/lmdeploy/turbomind/turbomind.py", line 387, in from_pretrained
    return cls(model_path=pretrained_model_name_or_path,
  File "/opt/lmdeploy/lmdeploy/turbomind/turbomind.py", line 161, in __init__
    self.model_comm = self._from_hf(model_source=model_source,
  File "/opt/lmdeploy/lmdeploy/turbomind/turbomind.py", line 270, in _from_hf
    output_model = OUTPUT_MODELS.get(output_format)(
  File "/opt/lmdeploy/lmdeploy/turbomind/deploy/target_model/fp.py", line 26, in __init__
    super().__init__(input_model, cfg, to_file, out_dir)
  File "/opt/lmdeploy/lmdeploy/turbomind/deploy/target_model/base.py", line 156, in __init__
    self.cfg = self.get_config(cfg)
  File "/opt/lmdeploy/lmdeploy/turbomind/deploy/target_model/fp.py", line 38, in get_config
    w1, _, _ = bin.ffn(i)
  File "/opt/lmdeploy/lmdeploy/turbomind/deploy/source_model/internlm2.py", line 69, in ffn
    return self._ffn(i, 'weight')
  File "/opt/lmdeploy/lmdeploy/turbomind/deploy/source_model/internlm2.py", line 62, in _ffn
    tensor = self.params[
KeyError: 'language_model.model.layers.0.feed_forward.w1.weight'

Can you provide more details including test code and environment configuration?

zwgao

OpenGVLab org Aug 21, 2024

I would still also prefer to run it directly without lmdeploy if possible, thanks!

Our awq model is generated by lmdeploy. If you want to use autoawq integrated by transformers, you need to make sure that the autoawq library supports our model.

czczup changed discussion status to closed Aug 22, 2024

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