Image-Text-to-Text
Transformers
GGUF
qwen3_5_text
llama.cpp
vision
multimodal
text-generation-inference
unsloth
conversational
qwen3_5
reasoning
chain-of-thought
lora
sft
agent
tool-use
function-calling
coder
Instructions to use Atomic-Germ/Qwopus3.5-9B-Coder-NPU with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Atomic-Germ/Qwopus3.5-9B-Coder-NPU with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Atomic-Germ/Qwopus3.5-9B-Coder-NPU") 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/Qwopus3.5-9B-Coder-NPU") model = AutoModelForMultimodalLM.from_pretrained("Atomic-Germ/Qwopus3.5-9B-Coder-NPU") 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]:])) - llama-cpp-python
How to use Atomic-Germ/Qwopus3.5-9B-Coder-NPU with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Atomic-Germ/Qwopus3.5-9B-Coder-NPU", filename="Qwopus3.5-9B-coder-Exp-BF16.gguf", )
llm.create_chat_completion( 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" } } ] } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Atomic-Germ/Qwopus3.5-9B-Coder-NPU 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/Qwopus3.5-9B-Coder-NPU:Q4_K_M # Run inference directly in the terminal: llama cli -hf Atomic-Germ/Qwopus3.5-9B-Coder-NPU:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Atomic-Germ/Qwopus3.5-9B-Coder-NPU:Q4_K_M # Run inference directly in the terminal: llama cli -hf Atomic-Germ/Qwopus3.5-9B-Coder-NPU:Q4_K_M
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/Qwopus3.5-9B-Coder-NPU:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Atomic-Germ/Qwopus3.5-9B-Coder-NPU:Q4_K_M
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/Qwopus3.5-9B-Coder-NPU:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Atomic-Germ/Qwopus3.5-9B-Coder-NPU:Q4_K_M
Use Docker
docker model run hf.co/Atomic-Germ/Qwopus3.5-9B-Coder-NPU:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Atomic-Germ/Qwopus3.5-9B-Coder-NPU with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Atomic-Germ/Qwopus3.5-9B-Coder-NPU" # 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/Qwopus3.5-9B-Coder-NPU", "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/Qwopus3.5-9B-Coder-NPU:Q4_K_M
- SGLang
How to use Atomic-Germ/Qwopus3.5-9B-Coder-NPU 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/Qwopus3.5-9B-Coder-NPU" \ --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/Qwopus3.5-9B-Coder-NPU", "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/Qwopus3.5-9B-Coder-NPU" \ --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/Qwopus3.5-9B-Coder-NPU", "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/Qwopus3.5-9B-Coder-NPU with Ollama:
ollama run hf.co/Atomic-Germ/Qwopus3.5-9B-Coder-NPU:Q4_K_M
- Unsloth Studio
How to use Atomic-Germ/Qwopus3.5-9B-Coder-NPU with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Atomic-Germ/Qwopus3.5-9B-Coder-NPU to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Atomic-Germ/Qwopus3.5-9B-Coder-NPU to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Atomic-Germ/Qwopus3.5-9B-Coder-NPU to start chatting
- Pi
How to use Atomic-Germ/Qwopus3.5-9B-Coder-NPU 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/Qwopus3.5-9B-Coder-NPU:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/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/Qwopus3.5-9B-Coder-NPU:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Atomic-Germ/Qwopus3.5-9B-Coder-NPU 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/Qwopus3.5-9B-Coder-NPU:Q4_K_M
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/Qwopus3.5-9B-Coder-NPU:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use Atomic-Germ/Qwopus3.5-9B-Coder-NPU 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/Qwopus3.5-9B-Coder-NPU:Q4_K_M
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/Qwopus3.5-9B-Coder-NPU:Q4_K_M" \ --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"
- Docker Model Runner
How to use Atomic-Germ/Qwopus3.5-9B-Coder-NPU with Docker Model Runner:
docker model run hf.co/Atomic-Germ/Qwopus3.5-9B-Coder-NPU:Q4_K_M
- Lemonade
How to use Atomic-Germ/Qwopus3.5-9B-Coder-NPU with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Atomic-Germ/Qwopus3.5-9B-Coder-NPU:Q4_K_M
Run and chat with the model
lemonade run user.Qwopus3.5-9B-Coder-NPU-Q4_K_M
List all available models
lemonade list
| { | |
| "architectures": [ | |
| "Qwen3_5ForConditionalGeneration" | |
| ], | |
| "image_token_id": 248056, | |
| "model_type": "qwen3_5", | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "attn_output_gate": true, | |
| "dtype": "bfloat16", | |
| "eos_token_id": 248044, | |
| "full_attention_interval": 4, | |
| "head_dim": 256, | |
| "hidden_act": "silu", | |
| "hidden_size": 4096, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 12288, | |
| "layer_types": [ | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention" | |
| ], | |
| "linear_conv_kernel_dim": 4, | |
| "linear_key_head_dim": 128, | |
| "linear_num_key_heads": 16, | |
| "linear_num_value_heads": 32, | |
| "linear_value_head_dim": 128, | |
| "max_position_embeddings": 262144, | |
| "mlp_only_layers": [], | |
| "model_type": "qwen3_5_text", | |
| "mtp_num_hidden_layers": 1, | |
| "mtp_use_dedicated_embeddings": false, | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 32, | |
| "num_key_value_heads": 4, | |
| "rms_norm_eps": 1e-06, | |
| "use_cache": true, | |
| "vocab_size": 248320, | |
| "mamba_ssm_dtype": "float32", | |
| "rope_parameters": { | |
| "mrope_interleaved": true, | |
| "mrope_section": [ | |
| 11, | |
| 11, | |
| 10 | |
| ], | |
| "rope_type": "default", | |
| "rope_theta": 10000000, | |
| "partial_rotary_factor": 0.25 | |
| }, | |
| "addr_qk": 53248, | |
| "addr_kv": 53536, | |
| "flm_version": "0.9.37", | |
| "vision_model_weight": "vision_weight.q4nx", | |
| "vision_end_token_id": 248054, | |
| "vision_start_token_id": 248053, | |
| "vision_config": { | |
| "vision_mm_engine_xclbin_name": "vision_mm.xclbin", | |
| "vision_mha_engine_xclbin_name": "vision_attn.xclbin", | |
| "QWEN3_5_PATCH_SIZE": 16, | |
| "QWEN3_5_IMAGE_MERGE_SIZE": 2, | |
| "QWEN3_5_SPATIAL_MERGE_SIZE": 2, | |
| "QWEN3_5_SHORTEST_EDGE": 65536, | |
| "QWEN3_5_LONGEST_EDGE": 16777216, | |
| "QWEN3_5_VISION_RESCALE_FACTOR": 0.00392156862745098, | |
| "QWEN3_5_VISION_RESCALE_IMAGE_MEAN": 0.5, | |
| "QWEN3_5_VISION_RESCALE_IMAGE_STD": 0.5, | |
| "QWEN3_5_TEMPORAL_PATCH_SIZE": 2, | |
| "QWEN3_5_VISION_EMBED_DIM": 1152, | |
| "QWEN3_5_VISION_NUM_HEADS": 16, | |
| "QWEN3_5_VISION_HEAD_DIM": 72, | |
| "QWEN3_5_VISION_MLP_INTERMEDIATE_SIZE": 4304, | |
| "QWEN3_5_VISION_NUM_POSITION_EMBEDDINGS": 2304, | |
| "QWEN3_5_VISION_NUM_LAYERS": 27, | |
| "QWEN3_5_VISION_LAYER_NORM_EPSILON": 1e-06, | |
| "QWEN3_5_VISION_OUT_HIDDEN_SIZE": 4096, | |
| "VISION_MM_TILE_M": 128, | |
| "VISION_MM_TILE_K": 256, | |
| "VISION_MM_TILE_N": 64 | |
| } | |
| } |