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

Libraries

How to use alphaedge-ai/Qwen3.5-2B-min-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-min-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/Qwen3.5-2B-min-16384")
model = AutoModelForMultimodalLM.from_pretrained("alphaedge-ai/Qwen3.5-2B-min-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/Qwen3.5-2B-min-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-min-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-min-16384",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

Qwen3.5-2B-min-16384

File size: 2,855 Bytes

ff598d3

---
pipeline_tag: text-generation
language: min
license: apache-2.0
tags:
  - trimmed
library_name: transformers
base_model: Qwen3.5-2B
base_model_relation: quantized
datasets:
  - lbourdois/fineweb-2-trimming
---

# Qwen3.5-2B-min-16384
This model is a **21.46% smaller** version of [Qwen/Qwen3.5-2B](https://huggingface.co/Qwen/Qwen3.5-2B) optimized for **Minangkabau** language via vocabulary size reduction using the [trimming](https://huggingface.co/blog/lbourdois/introduction-to-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 the selected languages were removed from the vocabulary.

## Model Statistics
| Metric | Original | Trimmed | Reduction |
|--------|----------|---------|-----------|
| **Vocabulary size** | 248,320 tokens | 16,384 tokens | **93.40%** |
| **Model size** | 2,213,241,664 params | 1,738,236,736 params | **21.46%** |

![image](https://raw.githubusercontent.com/lbourdois/blog/refs/heads/master/assets/images/Trimming/qwen.5-2B-16384.png)

## Mining Dataset Statistics
- **Number of texts used for mining**: 200,000 texts  
- **Dataset**: [lbourdois/fineweb-2-trimming](https://huggingface.co/datasets/lbourdois/fineweb-2-trimming)

## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alphaedge-ai/Qwen.5-2B-min-32768"

# 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 Minangkabau."
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)
```

## Citations

#### Qwen3
```
@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}
}
```

#### Trimming blog post
```
@misc{hf_blogpost_trimming,
      title={Introduction to Trimming}, 
      author={Loïck BOURDOIS and Tom AARSEN and Bram VANROY and Christopher AKIKI and Woojun JUNG and Manuel ROMERO and Prithiv SAKTHI},
      year={2026},
      url={https://huggingface.co/blog/lbourdois/introduction-to-trimming}, 
}
```