Instructions to use unsloth/Qwen2.5-VL-32B-Instruct-unsloth-bnb-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use unsloth/Qwen2.5-VL-32B-Instruct-unsloth-bnb-4bit with Transformers:

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

pipe = pipeline("image-text-to-text", model="unsloth/Qwen2.5-VL-32B-Instruct-unsloth-bnb-4bit")
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("unsloth/Qwen2.5-VL-32B-Instruct-unsloth-bnb-4bit")
model = AutoModelForMultimodalLM.from_pretrained("unsloth/Qwen2.5-VL-32B-Instruct-unsloth-bnb-4bit", 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 unsloth/Qwen2.5-VL-32B-Instruct-unsloth-bnb-4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "unsloth/Qwen2.5-VL-32B-Instruct-unsloth-bnb-4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "unsloth/Qwen2.5-VL-32B-Instruct-unsloth-bnb-4bit",
		"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/unsloth/Qwen2.5-VL-32B-Instruct-unsloth-bnb-4bit

SGLang

How to use unsloth/Qwen2.5-VL-32B-Instruct-unsloth-bnb-4bit 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 "unsloth/Qwen2.5-VL-32B-Instruct-unsloth-bnb-4bit" \
    --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": "unsloth/Qwen2.5-VL-32B-Instruct-unsloth-bnb-4bit",
		"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 "unsloth/Qwen2.5-VL-32B-Instruct-unsloth-bnb-4bit" \
        --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": "unsloth/Qwen2.5-VL-32B-Instruct-unsloth-bnb-4bit",
		"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"
						}
					}
				]
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use unsloth/Qwen2.5-VL-32B-Instruct-unsloth-bnb-4bit with Docker Model Runner:
```
docker model run hf.co/unsloth/Qwen2.5-VL-32B-Instruct-unsloth-bnb-4bit
```

danielhanchen commited on May 12, 2025

Commit

35e14a8

verified ·

1 Parent(s): 940bfaa

Upload folder using huggingface_hub

Browse files

Files changed (3) hide show

config.json +1 -41
generation_config.json +1 -1
tokenizer_config.json +2 -2

config.json CHANGED Viewed

@@ -121,48 +121,9 @@
   },
   "rope_theta": 1000000.0,
   "sliding_window": 32768,
-  "text_config": {
-    "architectures": [
-      "Qwen2_5_VLForConditionalGeneration"
-    ],
-    "attention_dropout": 0.0,
-    "eos_token_id": 151645,
-    "hidden_act": "silu",
-    "hidden_size": 5120,
-    "image_token_id": null,
-    "initializer_range": 0.02,
-    "intermediate_size": 27648,
-    "max_position_embeddings": 128000,
-    "max_window_layers": 64,
-    "model_type": "qwen2_5_vl_text",
-    "num_attention_heads": 40,
-    "num_hidden_layers": 64,
-    "num_key_value_heads": 8,
-    "pad_token_id": 151643,
-    "rms_norm_eps": 1e-06,
-    "rope_scaling": {
-      "mrope_section": [
-        16,
-        24,
-        24
-      ],
-      "rope_type": "default",
-      "type": "default"
-    },
-    "rope_theta": 1000000.0,
-    "sliding_window": 32768,
-    "torch_dtype": "bfloat16",
-    "use_cache": true,
-    "use_sliding_window": false,
-    "video_token_id": null,
-    "vision_end_token_id": 151653,
-    "vision_start_token_id": 151652,
-    "vision_token_id": 151654,
-    "vocab_size": 152064
-  },
   "tie_word_embeddings": false,
   "torch_dtype": "bfloat16",
-  "transformers_version": "4.52.0.dev0",
   "unsloth_fixed": true,
   "use_cache": true,
   "use_sliding_window": false,
@@ -179,7 +140,6 @@
     "hidden_size": 1280,
     "in_channels": 3,
     "in_chans": 3,
-    "initializer_range": 0.02,
     "intermediate_size": 3456,
     "model_type": "qwen2_5_vl",
     "num_heads": 16,

   },
   "rope_theta": 1000000.0,
   "sliding_window": 32768,
   "tie_word_embeddings": false,
   "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.3",
   "unsloth_fixed": true,
   "use_cache": true,
   "use_sliding_window": false,
     "hidden_size": 1280,
     "in_channels": 3,
     "in_chans": 3,
     "intermediate_size": 3456,
     "model_type": "qwen2_5_vl",
     "num_heads": 16,

generation_config.json CHANGED Viewed

@@ -9,5 +9,5 @@
   "pad_token_id": 151654,
   "repetition_penalty": 1.05,
   "temperature": 1e-06,
-  "transformers_version": "4.52.0.dev0"
 }

   "pad_token_id": 151654,
   "repetition_penalty": 1.05,
   "temperature": 1e-06,
+  "transformers_version": "4.51.3"
 }

tokenizer_config.json CHANGED Viewed

@@ -195,6 +195,7 @@
     "<|video_pad|>"
   ],
   "bos_token": null,
   "clean_up_tokenization_spaces": false,
   "eos_token": "<|im_end|>",
   "errors": "replace",
@@ -205,6 +206,5 @@
   "processor_class": "Qwen2_5_VLProcessor",
   "split_special_tokens": false,
   "tokenizer_class": "Qwen2Tokenizer",
-  "unk_token": null,
-  "chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
 }

     "<|video_pad|>"
   ],
   "bos_token": null,
+  "chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}",
   "clean_up_tokenization_spaces": false,
   "eos_token": "<|im_end|>",
   "errors": "replace",
   "processor_class": "Qwen2_5_VLProcessor",
   "split_special_tokens": false,
   "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
 }