Instructions to use prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX with Transformers:

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

pipe = pipeline("image-text-to-text", model="prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX")
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("prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX")
model = AutoModelForMultimodalLM.from_pretrained("prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX", 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 prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX",
		"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/prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX

SGLang

How to use prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX 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 "prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX" \
    --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": "prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX",
		"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 "prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX" \
        --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": "prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX",
		"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 prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX
```

prithivMLmods commited on Jun 1

Commit

7ba8e8c

verified ·

1 Parent(s): 69dcda7

Update README.md

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@@ -23,24 +23,24 @@ tags:
 # **Qwen3-VL-4B-Instruct-Unredacted-MAX**
-> **Qwen3-VL-4B-Instruct-Unredacted-MAX** is an optimized release built on top of **huihui-ai/Qwen3-VL-4B-Instruct-abliterated**. This version focuses on **improved packaging, updated Transformers compatibility, and stable multimodal inference behavior**, while preserving the core vision-language reasoning capabilities of the original architecture. The result is a capable **4B vision-language model** designed for efficient deployment, research experimentation, and multimodal application development.
 ## Key Highlights
-* **Optimized Release Packaging**
-  Streamlined repository structure for smoother loading, inference, and deployment workflows.
 * **Modern Transformers Compatibility**
-  Updated for stable integration with recent Hugging Face Transformers ecosystems.
 * **4B Vision-Language Architecture**
-  Built on **Qwen3-VL-4B-Instruct**, balancing multimodal capability with relatively low compute requirements.
 * **Stable Multimodal Inference**
-  Designed for consistent performance across image-text reasoning tasks.
 * **Efficient Caption Generation**
-  Produces structured and descriptive outputs suitable for annotation pipelines and dataset creation.
 * **Dynamic Resolution Support**
   Retains native support for varying image resolutions and aspect ratios.
@@ -50,13 +50,13 @@ tags:
 ## Base Model Signatures:
 This model has been re-sharded and optimized for the latest Transformers version from the base model:
-https://huggingface.co/huihui-ai/Qwen3-VL-4B-Instruct-abliterated
 ---
 ## Quick Start with Transformers
-```python id="vl4b_code"
 from transformers import Qwen3VLForConditionalGeneration, AutoProcessor
 from qwen_vl_utils import process_vision_info
 import torch
@@ -113,19 +113,19 @@ print(output_text)
 ## Intended Use
-* Multimodal AI research and evaluation
-* Vision-language dataset generation and annotation
-* Lightweight deployment on consumer GPUs
-* Prototyping multimodal applications and tools
-* Robustness testing and model benchmarking
 ---
 ## Limitations & Risks
-> **Important Note**: This model inherits limitations from its base architecture.
-* Output quality depends on image clarity and prompt design
-* May produce incomplete or inaccurate interpretations in complex scenarios
-* Requires sufficient VRAM for stable inference
-* Performance varies with decoding strategy and runtime optimizations

 # **Qwen3-VL-4B-Instruct-Unredacted-MAX**
+> **Qwen3-VL-4B-Instruct-Unredacted-MAX** is an optimized release built on top of **huihui-ai/Qwen3-VL-4B-Instruct-abliterated**. This version focuses on **updated packaging, improved Transformers compatibility, and stable multimodal inference behavior**, while preserving the core vision-language reasoning capabilities of the original architecture. The result is a capable **4B vision-language model** designed for efficient deployment, research workflows, and multimodal experimentation.
 ## Key Highlights
+* **Optimized Release Structure**
+  Streamlined repository organization for easier loading, deployment, and inference workflows.
 * **Modern Transformers Compatibility**
+  Updated for stable integration with recent Hugging Face Transformers versions.
 * **4B Vision-Language Architecture**
+  Built on **Qwen3-VL-4B-Instruct**, balancing multimodal capability with efficient compute requirements.
 * **Stable Multimodal Inference**
+  Designed for consistent performance across image-text understanding tasks.
 * **Efficient Caption Generation**
+  Produces structured and detailed descriptions suitable for annotation and dataset pipelines.
 * **Dynamic Resolution Support**
   Retains native support for varying image resolutions and aspect ratios.
 ## Base Model Signatures:
 This model has been re-sharded and optimized for the latest Transformers version from the base model:
+https://huggingface.co/huihui-ai/Huihui-Qwen3-VL-4B-Instruct-abliterated
 ---
 ## Quick Start with Transformers
+```python id="q3vl4b_code"
 from transformers import Qwen3VLForConditionalGeneration, AutoProcessor
 from qwen_vl_utils import process_vision_info
 import torch
 ## Intended Use
+* Multimodal research and vision-language evaluation
+* Image captioning and dataset generation pipelines
+* Prototyping AI systems combining text and vision
+* Lightweight deployment on consumer or mid-range GPUs
+* Experimental workflows in multimodal understanding
 ---
 ## Limitations & Risks
+> **Important Note**: This model inherits constraints and behavior from its base architecture.
+* Output quality depends heavily on image clarity and prompt design
+* May produce incomplete or inconsistent interpretations in complex scenarios
+* Requires sufficient GPU memory for stable inference
+* Performance varies with decoding settings and runtime optimization