Instructions to use alphaedge-ai/Qwen3.5-4B-fin-16384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alphaedge-ai/Qwen3.5-4B-fin-16384 with Transformers:

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

pipe = pipeline("text-generation", model="alphaedge-ai/Qwen3.5-4B-fin-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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("alphaedge-ai/Qwen3.5-4B-fin-16384")
model = AutoModelForMultimodalLM.from_pretrained("alphaedge-ai/Qwen3.5-4B-fin-16384", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use alphaedge-ai/Qwen3.5-4B-fin-16384 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alphaedge-ai/Qwen3.5-4B-fin-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/Qwen3.5-4B-fin-16384",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/alphaedge-ai/Qwen3.5-4B-fin-16384

SGLang

How to use alphaedge-ai/Qwen3.5-4B-fin-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/Qwen3.5-4B-fin-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/Qwen3.5-4B-fin-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/Qwen3.5-4B-fin-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/Qwen3.5-4B-fin-16384",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use alphaedge-ai/Qwen3.5-4B-fin-16384 with Docker Model Runner:
```
docker model run hf.co/alphaedge-ai/Qwen3.5-4B-fin-16384
```

Qwen3.5-4B-fin-16384 / README.md

lbourdois

Upload 10 files

6caab05 verified 5 months ago

preview code

Raw History Blame

2.57 kB

metadata

language: fin
license: apache-2.0
tags:
  - trimmed
  - qwen3.5
base_model: Qwen/Qwen3.5-4B
base_model_relation: quantized
datasets:
  - Lumberjackk/fineweb-2-trimming

Qwen3.5-4B-fin-16384

This model is a 13.08% smaller version of Qwen/Qwen3.5-4B optimized for Finnish language via vocabulary size reduction using the trimming method.

This trimmed model should perform similarly to the original model with only 16,384 tokens and a much smaller memory footprint. However, it may not perform well for other languages as tokens not commonly used in Finnish were removed from the vocabulary.

Note: Qwen3.5 is a multimodal (vision-language) model. This trimmed version retains the vision encoder but reduces only the text vocabulary.

Model Statistics

Metric	Original	Trimmed	Reduction
Vocabulary size	248,044 tokens	16,384 tokens	93.39%
Model size	4,539,265,536 params	3,945,509,376 params	13.08%

Mining Dataset Statistics

Number of texts used for mining: 200,000 texts
Dataset: Lumberjackk/fineweb-2-trimming

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "AlphaEdge-AI/Qwen3.5-4B-fin-16384"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True
)

# prepare the model input
prompt = "Your prompt in Finnish."
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):]
content = tokenizer.decode(output_ids, skip_special_tokens=True)

print("content:", content)

Citation

Qwen3.5

@misc{qwen3.5,
    title  = {Qwen3.5: Towards Native Multimodal Agents},
    author = {Qwen Team},
    month  = {February},
    year   = {2026},
    url    = {https://qwen.ai/blog?id=qwen3.5}
}