Instructions to use BCCard/Qwen2.5-VL-32B-Instruct-FP8-Dynamic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BCCard/Qwen2.5-VL-32B-Instruct-FP8-Dynamic with Transformers:

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

pipe = pipeline("image-text-to-text", model="BCCard/Qwen2.5-VL-32B-Instruct-FP8-Dynamic")
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 AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("BCCard/Qwen2.5-VL-32B-Instruct-FP8-Dynamic")
model = AutoModelForMultimodalLM.from_pretrained("BCCard/Qwen2.5-VL-32B-Instruct-FP8-Dynamic", device_map="auto")
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?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BCCard/Qwen2.5-VL-32B-Instruct-FP8-Dynamic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BCCard/Qwen2.5-VL-32B-Instruct-FP8-Dynamic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BCCard/Qwen2.5-VL-32B-Instruct-FP8-Dynamic",
		"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/BCCard/Qwen2.5-VL-32B-Instruct-FP8-Dynamic

SGLang

How to use BCCard/Qwen2.5-VL-32B-Instruct-FP8-Dynamic 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 "BCCard/Qwen2.5-VL-32B-Instruct-FP8-Dynamic" \
    --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": "BCCard/Qwen2.5-VL-32B-Instruct-FP8-Dynamic",
		"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 "BCCard/Qwen2.5-VL-32B-Instruct-FP8-Dynamic" \
        --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": "BCCard/Qwen2.5-VL-32B-Instruct-FP8-Dynamic",
		"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 BCCard/Qwen2.5-VL-32B-Instruct-FP8-Dynamic with Docker Model Runner:
```
docker model run hf.co/BCCard/Qwen2.5-VL-32B-Instruct-FP8-Dynamic
```

sh2orc commited on Jun 20, 2025

Commit

a3dcd69

verified ·

1 Parent(s): 9a0e866

Update README.md

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README.md +5 -5

README.md CHANGED Viewed

@@ -8,22 +8,22 @@ license_link: >-
   https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md
 language:
 - en
-base_model: Qwen/Qwen2.5-VL-72B-Instruct
 library_name: transformers
 ---
 # Qwen2.5-VL-32B-Instruct-FP8-Dynamic
 ## Model Overview
-- **Model Architecture:** Qwen2.5-VL-72B-Instruct
   - **Input:** Vision-Text
   - **Output:** Text
 - **Model Optimizations:**
   - **Weight quantization:** FP8
   - **Activation quantization:** FP8
-- **Release Date:** 2/24/2025
 - **Version:** 1.0
-- **Model Developers:** Neural Magic
 Quantized version of [Qwen/Qwen2.5-VL-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-32B-Instruct).
@@ -43,7 +43,7 @@ from vllm import LLM, SamplingParams
 # prepare model
 llm = LLM(
-    model="BCCard/Qwen2.5-VL-72B-Instruct-FP8-Dynamic",
     trust_remote_code=True,
     max_model_len=4096,
     max_num_seqs=2,

   https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md
 language:
 - en
+base_model: Qwen/Qwen2.5-VL-32B-Instruct
 library_name: transformers
 ---
 # Qwen2.5-VL-32B-Instruct-FP8-Dynamic
 ## Model Overview
+- **Model Architecture:** Qwen2.5-VL-32B-Instruct
   - **Input:** Vision-Text
   - **Output:** Text
 - **Model Optimizations:**
   - **Weight quantization:** FP8
   - **Activation quantization:** FP8
+- **Release Date:** 5/3/2025
 - **Version:** 1.0
+- **Model Developers:** BC Card
 Quantized version of [Qwen/Qwen2.5-VL-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-32B-Instruct).
 # prepare model
 llm = LLM(
+    model="BCCard/Qwen2.5-VL-32B-Instruct-FP8-Dynamic",
     trust_remote_code=True,
     max_model_len=4096,
     max_num_seqs=2,