Instructions to use Atomic-Germ/Qwen3.8-Distilled-2B-NPU2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Atomic-Germ/Qwen3.8-Distilled-2B-NPU2 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-Distilled-2B-NPU2")
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 AutoModel
model = AutoModel.from_pretrained("Atomic-Germ/Qwen3.8-Distilled-2B-NPU2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Atomic-Germ/Qwen3.8-Distilled-2B-NPU2 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Atomic-Germ/Qwen3.8-Distilled-2B-NPU2:Q8_0
# Run inference directly in the terminal:
llama cli -hf Atomic-Germ/Qwen3.8-Distilled-2B-NPU2:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Atomic-Germ/Qwen3.8-Distilled-2B-NPU2:Q8_0
# Run inference directly in the terminal:
llama cli -hf Atomic-Germ/Qwen3.8-Distilled-2B-NPU2:Q8_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Atomic-Germ/Qwen3.8-Distilled-2B-NPU2:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Atomic-Germ/Qwen3.8-Distilled-2B-NPU2:Q8_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Atomic-Germ/Qwen3.8-Distilled-2B-NPU2:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Atomic-Germ/Qwen3.8-Distilled-2B-NPU2:Q8_0

Use Docker

docker model run hf.co/Atomic-Germ/Qwen3.8-Distilled-2B-NPU2:Q8_0

LM Studio
Jan

vLLM

How to use Atomic-Germ/Qwen3.8-Distilled-2B-NPU2 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-Distilled-2B-NPU2"
# 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-Distilled-2B-NPU2",
		"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-Distilled-2B-NPU2:Q8_0

SGLang

How to use Atomic-Germ/Qwen3.8-Distilled-2B-NPU2 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-Distilled-2B-NPU2" \
    --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-Distilled-2B-NPU2",
		"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-Distilled-2B-NPU2" \
        --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-Distilled-2B-NPU2",
		"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"
						}
					}
				]
			}
		]
	}'

Ollama
How to use Atomic-Germ/Qwen3.8-Distilled-2B-NPU2 with Ollama:
```
ollama run hf.co/Atomic-Germ/Qwen3.8-Distilled-2B-NPU2:Q8_0
```
Unsloth Desktop

How to use Atomic-Germ/Qwen3.8-Distilled-2B-NPU2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Atomic-Germ/Qwen3.8-Distilled-2B-NPU2:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Atomic-Germ/Qwen3.8-Distilled-2B-NPU2:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Atomic-Germ/Qwen3.8-Distilled-2B-NPU2 with Docker Model Runner:
```
docker model run hf.co/Atomic-Germ/Qwen3.8-Distilled-2B-NPU2:Q8_0
```

Lemonade

How to use Atomic-Germ/Qwen3.8-Distilled-2B-NPU2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Atomic-Germ/Qwen3.8-Distilled-2B-NPU2:Q8_0

Run and chat with the model

lemonade run user.Qwen3.8-Distilled-2B-NPU2-Q8_0

List all available models

lemonade list

Hermes Agent

How to use Atomic-Germ/Qwen3.8-Distilled-2B-NPU2 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Atomic-Germ/Qwen3.8-Distilled-2B-NPU2:Q8_0

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Atomic-Germ/Qwen3.8-Distilled-2B-NPU2:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Atomic-Germ/Qwen3.8-Distilled-2B-NPU2 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Atomic-Germ/Qwen3.8-Distilled-2B-NPU2:Q8_0

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Atomic-Germ/Qwen3.8-Distilled-2B-NPU2:Q8_0" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Qwen3.8-Distilled-2B-NPU2 / flm-test.log

Atomic-Germ

Upload flm-test.log with huggingface_hub

834b5a8 verified about 1 month ago

Raw

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	Please ensure you have started the FLM server and have the correct URL and port.


	Checking flm version...
	Detected flm version: 1.0.1

	Fetching available models...

	=== Starting LLM Tests ===
	Models found: 1

	--- Testing LLM model: qwen3.8-distill:2b ---

	Testing stream mode...

	Prompt: Teach me Maxwell's equations.
	Done.
	Follow-up Prompt: Explain why they are important.
	Done.
	Finished testing model: qwen3.8-distill:2b

	LLM tests complete. Saved to results/20260824_112808/linux/llm_results_v1.0.1.csv

	Checking flm version...
	Detected flm version: 1.0.1

	Fetching available models...
	1. qwen3.8-distill:2b

	=== Starting Vision Tests ===
	Models found: 1

	--- Testing VLMs: qwen3.8-distill:2b ---
	Prompt: Extract text from the first image, describe the second one, and imagine what the spectrogram might sound like.
	Text extraction check: PASS
	Done.
	Follow-up Prompt: Make a story that connects the images together.
	Seagull check: PASS (recognized in description)
	Done.
	Follow-up Prompt: What kind of sound does the spectrogram represent?
	Spectrogram music check: PASS
	Done.
	Finished testing model: qwen3.8-distill:2b
	Vision tests complete. Saved to results/20260824_112950/linux/vision_results_v1.0.1.csv

	Checking flm version...
	Detected flm version: 1.0.1

	Fetching available models...

	=== Starting Tool Calling Tests ===
	Models found: 1
	Reasoning effort requested: medium

	--- Testing tool-calling model: qwen3.8-distill:2b ---

	Testing non-stream mode...
	L1 Basic Tool Call: What's the current weather in Paris right now? Use the tools available to you.
	Check result: FAIL (no tool call issued)
	L2 Argument Extraction: A friend of mine lives in the city where the Eiffel Tower stands. Use your tools to tell me the current weather there.
	Check result: PASS (called get_current_weather with location 'Paris')
	L3 Tool Restraint: Do not call any tools. Answer directly from your own knowledge: what is the capital of France?
	Check result: PASS (answered directly without any tool call)
	L4 Parallel Tool Calls: Using your tools, compare the current weather in Paris and Tokyo.
	Check result: FAIL (no tool call issued)
	L5 Multi-Turn Tool Loop: Use the price lookup tool to check the unit price of a 'widget', then tell me what 3 widgets would cost after a 10% discount. Do the math yourself.
	Lookup check result: PASS (requested price for 'widget')
	Final answer check result: PASS (final answer contains the computed total ($54))

	Testing stream mode...
	L1 Basic Tool Call: What's the current weather in Paris right now? Use the tools available to you.
	Check result: PASS (called get_current_weather with location 'Paris')
	L2 Argument Extraction: A friend of mine lives in the city where the Eiffel Tower stands. Use your tools to tell me the current weather there.
	Check result: PASS (called get_current_weather with location 'Paris, France')
	L3 Tool Restraint: Do not call any tools. Answer directly from your own knowledge: what is the capital of France?
	Check result: PASS (answered directly without any tool call)
	L4 Parallel Tool Calls: Using your tools, compare the current weather in Paris and Tokyo.
	Check result: FAIL (no tool call issued)
	L5 Multi-Turn Tool Loop: Use the price lookup tool to check the unit price of a 'widget', then tell me what 3 widgets would cost after a 10% discount. Do the math yourself.
	Lookup check result: FAIL (no tool call issued for the price lookup)
	Final answer check result: FAIL (computed total $54 not found in final answer)
	Finished testing model: qwen3.8-distill:2b

	Tool calling tests complete. Saved to results/20260824_113114/linux/tools_results_v1.0.1.csv