Instructions to use Elsephire/Qwen3.5-0.8B-vocabulary-trimming with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Elsephire/Qwen3.5-0.8B-vocabulary-trimming with Transformers:

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

pipe = pipeline("image-text-to-text", model="Elsephire/Qwen3.5-0.8B-vocabulary-trimming")
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("Elsephire/Qwen3.5-0.8B-vocabulary-trimming")
model = AutoModelForMultimodalLM.from_pretrained("Elsephire/Qwen3.5-0.8B-vocabulary-trimming")
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 Elsephire/Qwen3.5-0.8B-vocabulary-trimming with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Elsephire/Qwen3.5-0.8B-vocabulary-trimming"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Elsephire/Qwen3.5-0.8B-vocabulary-trimming",
		"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/Elsephire/Qwen3.5-0.8B-vocabulary-trimming

SGLang

How to use Elsephire/Qwen3.5-0.8B-vocabulary-trimming 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 "Elsephire/Qwen3.5-0.8B-vocabulary-trimming" \
    --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": "Elsephire/Qwen3.5-0.8B-vocabulary-trimming",
		"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 "Elsephire/Qwen3.5-0.8B-vocabulary-trimming" \
        --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": "Elsephire/Qwen3.5-0.8B-vocabulary-trimming",
		"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 Elsephire/Qwen3.5-0.8B-vocabulary-trimming with Docker Model Runner:
```
docker model run hf.co/Elsephire/Qwen3.5-0.8B-vocabulary-trimming
```

Qwen3.5-0.8B-vocabulary-trimming

File size: 1,446 Bytes

a30e544

{
  "version": "1.1.0",
  "model": "Qwen/Qwen3.5-0.8B",
  "output": "D:\\Minerve\\creation models\\models\\Qwen_Qwen3.5-0.8B\\trimmed",
  "old_vocab": 248077,
  "new_vocab": 145572,
  "padded_vocab": 145572,
  "keep_scripts": [
    "Greek",
    "Latin",
    "Unknown"
  ],
  "remove_scripts": [
    "Arabic",
    "Armenian",
    "Bengali",
    "CJK",
    "Cyrillic",
    "Devanagari",
    "Ethiopic",
    "Georgian",
    "Gujarati",
    "Gurmukhi",
    "Hebrew",
    "Kannada",
    "Khmer",
    "Lao",
    "Malayalam",
    "Mongolian",
    "Myanmar",
    "Sinhala",
    "Tamil",
    "Telugu",
    "Thai",
    "Tibetan"
  ],
  "script_counts": {
    "Latin": 144029,
    "Cyrillic": 18580,
    "Arabic": 8817,
    "CJK": 65722,
    "Thai": 5741,
    "Greek": 1543,
    "Devanagari": 959,
    "Hebrew": 520,
    "Bengali": 531,
    "Tamil": 268,
    "Malayalam": 205,
    "Khmer": 79,
    "Georgian": 186,
    "Telugu": 188,
    "Kannada": 144,
    "Myanmar": 147,
    "Armenian": 88,
    "Gujarati": 116,
    "Sinhala": 77,
    "Gurmukhi": 65,
    "Tibetan": 10,
    "Lao": 37,
    "Ethiopic": 25
  },
  "savings": {
    "old_vocab": 248077,
    "new_vocab": 145572,
    "vocab_reduction_pct": 41.319832148889255,
    "saved_mb": 400.41015625,
    "total_mb": 3876.203125,
    "size_reduction_pct": 10.329958037222314,
    "is_moe": false,
    "total_params": 2032246784
  }
}