Instructions to use Atomic-Germ/Qwen3.8-0.8B-Distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Atomic-Germ/Qwen3.8-0.8B-Distilled with Transformers:

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

pipe = pipeline("image-text-to-text", model="Atomic-Germ/Qwen3.8-0.8B-Distilled")
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 AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Atomic-Germ/Qwen3.8-0.8B-Distilled")
model = AutoModelForMultimodalLM.from_pretrained("Atomic-Germ/Qwen3.8-0.8B-Distilled", 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 = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Atomic-Germ/Qwen3.8-0.8B-Distilled with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Atomic-Germ/Qwen3.8-0.8B-Distilled"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Atomic-Germ/Qwen3.8-0.8B-Distilled",
		"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/Atomic-Germ/Qwen3.8-0.8B-Distilled

SGLang

How to use Atomic-Germ/Qwen3.8-0.8B-Distilled 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 "Atomic-Germ/Qwen3.8-0.8B-Distilled" \
    --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": "Atomic-Germ/Qwen3.8-0.8B-Distilled",
		"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 "Atomic-Germ/Qwen3.8-0.8B-Distilled" \
        --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": "Atomic-Germ/Qwen3.8-0.8B-Distilled",
		"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 Atomic-Germ/Qwen3.8-0.8B-Distilled with Docker Model Runner:
```
docker model run hf.co/Atomic-Germ/Qwen3.8-0.8B-Distilled
```

Qwen3.8-Distilled-0.8B-NPU2

FastFlowLM Q4NX conversion of gatilin/Qwen3.8-0.8B-Agentic-distill for AMD XDNA NPU inference.

This repository contains a quantized Q4NX port of the model, compiled for the FastFlowLM (FLM) runtime. It is not a GGUF file.

Item	Value
Source model	`gatilin/Qwen3.8-0.8B-Agentic-distill`
Weights	`model.q4nx` (1.02 GB)
Modality	language
FLM version	`1.0.1`
Converted	2026-08-29

Install and run

This repository works with flm-add, a small installer that copies the model into the FastFlowLM user directory and registers the tag. It never modifies the system FastFlowLM install.

pip install flm-add or uv tool install flm-add

uv tool install flm-add
flm-add Atomic-Germ/Qwen3.8-Distilled-0.8B-NPU2 --family qwen3.5
FLM_CONFIG_PATH="$HOME/.config/flm/model_list.json" FLM_XCLBIN_PATH="$HOME/.config/flm" flm run qwen3.8-distilled:0.8b

Files

File	Description
`model.q4nx`	Quantized weights (Q8_0 / Q4_1 / BF16)
`config.json`	FLM runtime configuration
`tokenizer.json`	Tokenizer vocabulary
`tokenizer_config.json`	Tokenizer configuration
`chat_template.jinja`	Chat template

Source model card

See the original model card: gatilin/Qwen3.8-0.8B-Agentic-distill on Hugging Face

Downloads last month: -

Safetensors

Model size

0.9B params

Tensor type

BF16

Model tree for Atomic-Germ/Qwen3.8-0.8B-Distilled

Base model

gatilin/Qwen3.8-0.8B-Agentic-distill

Quantized

(1)

this model