Instructions to use alphaedge-ai/Qwen3.5-0.8B-min-16384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alphaedge-ai/Qwen3.5-0.8B-min-16384 with Transformers:

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

pipe = pipeline("text-generation", model="alphaedge-ai/Qwen3.5-0.8B-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)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("alphaedge-ai/Qwen3.5-0.8B-min-16384")
model = AutoModelForImageTextToText.from_pretrained("alphaedge-ai/Qwen3.5-0.8B-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?"}
        ]
    },
]
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/Qwen3.5-0.8B-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/Qwen3.5-0.8B-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/Qwen3.5-0.8B-min-16384",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/alphaedge-ai/Qwen3.5-0.8B-min-16384

SGLang

How to use alphaedge-ai/Qwen3.5-0.8B-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/Qwen3.5-0.8B-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/Qwen3.5-0.8B-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/Qwen3.5-0.8B-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/Qwen3.5-0.8B-min-16384",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use alphaedge-ai/Qwen3.5-0.8B-min-16384 with Docker Model Runner:
```
docker model run hf.co/alphaedge-ai/Qwen3.5-0.8B-min-16384
```

Qwen3.5-0.8B-min-16384 / README.md

lbourdois

Update model card for Minangkabau

bc77b83 verified 17 days ago

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	---
	pipeline_tag: text-generation
	language: min
	license: apache-2.0
	tags:
	- trimmed
	library_name: transformers
	base_model: Qwen3.5-0.8B
	base_model_relation: quantized
	datasets:
	- lbourdois/fineweb-2-trimming
	---

	# Qwen3.5-0.8B-min-16384
	This model is a 27.84% smaller version of [Qwen/Qwen3.5-0.8B](https://huggingface.co/Qwen/Qwen3.5-0.8B) 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 \| 852,985,920 params \| 615,483,456 params \| 27.84% \|

	![image](https://raw.githubusercontent.com/lbourdois/blog/refs/heads/master/assets/images/Trimming/qwen.5-0.8B-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.5-0.8B-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},
	}
	```