Instructions to use alphaedge-ai/Qwen3.5-2B-jpn-16384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alphaedge-ai/Qwen3.5-2B-jpn-16384 with Transformers:

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

pipe = pipeline("text-generation", model="alphaedge-ai/Qwen3.5-2B-jpn-16384")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("alphaedge-ai/Qwen3.5-2B-jpn-16384")
model = AutoModelForImageTextToText.from_pretrained("alphaedge-ai/Qwen3.5-2B-jpn-16384")
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 alphaedge-ai/Qwen3.5-2B-jpn-16384 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alphaedge-ai/Qwen3.5-2B-jpn-16384"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alphaedge-ai/Qwen3.5-2B-jpn-16384",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/alphaedge-ai/Qwen3.5-2B-jpn-16384

SGLang

How to use alphaedge-ai/Qwen3.5-2B-jpn-16384 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 "alphaedge-ai/Qwen3.5-2B-jpn-16384" \
    --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": "alphaedge-ai/Qwen3.5-2B-jpn-16384",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "alphaedge-ai/Qwen3.5-2B-jpn-16384" \
        --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": "alphaedge-ai/Qwen3.5-2B-jpn-16384",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use alphaedge-ai/Qwen3.5-2B-jpn-16384 with Docker Model Runner:
```
docker model run hf.co/alphaedge-ai/Qwen3.5-2B-jpn-16384
```

Qwen3.5-2B-jpn-16384 / model.safetensors

lbourdois

Trimmed Qwen/Qwen3.5-2B for Japanese (16384 tokens)

fecb74e verified about 1 month ago

download

history blame contribute delete

3.48 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 9d9723e2ac7ab07b494f3fa2a07df4667596e79ace4be6fbe192e05865b5b6ba
Size of remote file:: 3.48 GB
SHA256:: 13fc7dc2ee2eba5f64f58160c825800aa04e288817f642129443a7a06a0fcf5e

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.