Qwen3-VL-4B-English-Thinking

Qwen3-VL-4B-English-Thinking

An English-only, vocabulary-pruned version of Qwen3-VL-4B-Thinking with 30.7% smaller vocabulary and more efficient tokenization for English text.

Model Specifications

Key Benefits

1. Effective Larger Context for English

Since non-English tokens are removed, English text tokenizes more efficiently:

• Same text uses fewer tokens
• 262K context window goes further for English content
• Estimated 20-30% more English text fits in context

2. Faster Tokenization

• Smaller vocabulary = faster token lookup
• Reduced embedding table size
• Marginally faster inference

3. Preserved Capabilities

• All English language capabilities
• Programming/code tokens (ASCII preserved)
• JSON, XML, Markdown support
• Vision-language multimodal
• Chain-of-thought reasoning (think tokens)
• Tool calling support

Technical Details

Architecture

Vision Encoder

Vocabulary Pruning Details

Special Tokens Preserved

All special tokens are preserved for full functionality:

Usage

from transformers import Qwen2VLForConditionalGeneration, AutoProcessor

model = Qwen2VLForConditionalGeneration.from_pretrained(
    "DavidrPatton/Qwen3-VL-4B-English-Thinking",
    torch_dtype="auto",
    device_map="auto"
)
processor = AutoProcessor.from_pretrained(
    "DavidrPatton/Qwen3-VL-4B-English-Thinking"
)

# Text-only example
messages = [{"role": "user", "content": "Explain quantum computing in simple terms."}]

text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=text, return_tensors="pt").to(model.device)

output = model.generate(**inputs, max_new_tokens=512)
print(processor.decode(output[0], skip_special_tokens=True))

With Images

from PIL import Image

image = Image.open("example.jpg")

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": image},
            {"type": "text", "text": "Describe what you see in this image."}
        ]
    }
]

text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=text, images=[image], return_tensors="pt").to(model.device)

output = model.generate(**inputs, max_new_tokens=512)
print(processor.decode(output[0], skip_special_tokens=True))

Hardware Requirements

Recommended: NVIDIA RTX 4070 Ti or better (16GB VRAM)

Model Files

Limitations

• English Only: Non-English languages are not supported
• Not Fine-Tuned: This is a vocabulary-pruned version, not a fine-tuned model
• Token ID Remapping: Some applications may need the token_remapper for compatibility
• Inherits base model capabilities and limitations

How It Works

The vocabulary pruning process:

1. Analyzed all 151,669 tokens in the original Qwen3-VL vocabulary
2. Categorized tokens by script (ASCII, CJK, Cyrillic, Arabic, etc.)
3. Preserved ASCII tokens (English + programming), special tokens, and essential symbols
4. Removed 46,500 non-English tokens
5. Remapped token IDs to maintain contiguous vocabulary
6. Adjusted embedding layers to match new vocabulary size

License

Apache 2.0 - Same as the base Qwen model.

Acknowledgments

• Qwen Team for the excellent Qwen3-VL base model
• Based on Qwen/Qwen3-VL-4B-Thinking