Instructions to use FreedomIntelligence/HuatuoGPT-Vision-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FreedomIntelligence/HuatuoGPT-Vision-7B with Transformers:

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

pipe = pipeline("text-generation", model="FreedomIntelligence/HuatuoGPT-Vision-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("FreedomIntelligence/HuatuoGPT-Vision-7B", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use FreedomIntelligence/HuatuoGPT-Vision-7B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/FreedomIntelligence/HuatuoGPT-Vision-7B

SGLang

How to use FreedomIntelligence/HuatuoGPT-Vision-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 "FreedomIntelligence/HuatuoGPT-Vision-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": "FreedomIntelligence/HuatuoGPT-Vision-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 "FreedomIntelligence/HuatuoGPT-Vision-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": "FreedomIntelligence/HuatuoGPT-Vision-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FreedomIntelligence/HuatuoGPT-Vision-7B with Docker Model Runner:
```
docker model run hf.co/FreedomIntelligence/HuatuoGPT-Vision-7B
```

jymcc commited on Jun 29, 2024

Commit

34dfcdb

verified ·

1 Parent(s): 159f69d

Update README.md

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README.md +52 -52

README.md CHANGED Viewed

@@ -1,52 +1,52 @@
----
-license: apache-2.0
-datasets:
-- FreedomIntelligence/PubMedVision
-language:
-- en
-- zh
-pipeline_tag: text-generation
----
-<div align="center">
-<h1>
-  HuatuoGPT-Vision-7B
-</h1>
-</div>
-<div align="center">
-<a href="https://github.com/FreedomIntelligence/HuatuoGPT-Vision" target="_blank">GitHub</a> | <a href="https://arxiv.org/abs/2406.19280" target="_blank">Paper</a>
-</div>
-# <span id="Start">Introduction</span>
-HuatuoGPT-Vision is a multimodal LLM for medical applications, built with the [PubMedVision dataset](https://huggingface.co/datasets/FreedomIntelligence/PubMedVision). HuatuoGPT-Vision-7B is trained based on Qwen2-7B using the LLaVA-v1.5 architecture.
-# <span id="Start">Quick Start</span>
-1. Get the model inference code from [Github](https://github.com/FreedomIntelligence/HuatuoGPT-Vision).
-```bash
-git clone https://github.com/FreedomIntelligence/HuatuoGPT-Vision.git
-```
-2. Model inference
-```python
-query = 'What does the picture show?'
-image_paths = ['image_path1']
-from cli import HuatuoChatbot
-bot = HuatuoChatbot(huatuogpt_vision_model_path) # load the model
-output = bot.inference(query, image_paths) # generation
-print(output)  # Prints the model output
-```
-# <span id="Start">Citation</span>
-```
-@misc{chen2024huatuogptvisioninjectingmedicalvisual,
-      title={HuatuoGPT-Vision, Towards Injecting Medical Visual Knowledge into Multimodal LLMs at Scale},
-      author={Junying Chen and Ruyi Ouyang and Anningzhe Gao and Shunian Chen and Guiming Hardy Chen and Xidong Wang and Ruifei Zhang and Zhenyang Cai and Ke Ji and Guangjun Yu and Xiang Wan and Benyou Wang},
-      year={2024},
-      eprint={2406.19280},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV},
-      url={https://arxiv.org/abs/2406.19280},
-}
-```

+---
+license: apache-2.0
+datasets:
+- FreedomIntelligence/PubMedVision
+language:
+- en
+- zh
+pipeline_tag: text-generation
+---
+<div align="center">
+<h1>
+  HuatuoGPT-Vision-7B
+</h1>
+</div>
+<div align="center">
+<a href="https://github.com/FreedomIntelligence/HuatuoGPT-Vision" target="_blank">GitHub</a> | <a href="https://arxiv.org/abs/2406.19280" target="_blank">Paper</a>
+</div>
+# <span id="Start">Introduction</span>
+HuatuoGPT-Vision is a multimodal LLM for medical applications, built with the [PubMedVision dataset](https://huggingface.co/datasets/FreedomIntelligence/PubMedVision). HuatuoGPT-Vision-7B is trained based on Qwen2-7B using the LLaVA-v1.5 architecture.
+# <span id="Start">Quick Start</span>
+1. Get the model inference code from [Github](https://github.com/FreedomIntelligence/HuatuoGPT-Vision).
+```bash
+git clone https://github.com/FreedomIntelligence/HuatuoGPT-Vision.git
+```
+2. Model inference
+```python
+query = 'What does the picture show?'
+image_paths = ['image_path1']
+from cli import HuatuoChatbot
+bot = HuatuoChatbot(huatuogpt_vision_model_path) # loads the model
+output = bot.inference(query, image_paths) # generates
+print(output)  # Prints the model output
+```
+# <span id="Start">Citation</span>
+```
+@misc{chen2024huatuogptvisioninjectingmedicalvisual,
+      title={HuatuoGPT-Vision, Towards Injecting Medical Visual Knowledge into Multimodal LLMs at Scale},
+      author={Junying Chen and Ruyi Ouyang and Anningzhe Gao and Shunian Chen and Guiming Hardy Chen and Xidong Wang and Ruifei Zhang and Zhenyang Cai and Ke Ji and Guangjun Yu and Xiang Wan and Benyou Wang},
+      year={2024},
+      eprint={2406.19280},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2406.19280},
+}
+```