Instructions to use Mer0vin8ian/Qwen3.5-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mer0vin8ian/Qwen3.5-9B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Mer0vin8ian/Qwen3.5-9B") 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("Mer0vin8ian/Qwen3.5-9B") model = AutoModelForMultimodalLM.from_pretrained("Mer0vin8ian/Qwen3.5-9B", 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 Mer0vin8ian/Qwen3.5-9B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Mer0vin8ian/Qwen3.5-9B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Mer0vin8ian/Qwen3.5-9B", "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/Mer0vin8ian/Qwen3.5-9B
- SGLang
How to use Mer0vin8ian/Qwen3.5-9B 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 "Mer0vin8ian/Qwen3.5-9B" \ --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": "Mer0vin8ian/Qwen3.5-9B", "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 "Mer0vin8ian/Qwen3.5-9B" \ --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": "Mer0vin8ian/Qwen3.5-9B", "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 Mer0vin8ian/Qwen3.5-9B with Docker Model Runner:
docker model run hf.co/Mer0vin8ian/Qwen3.5-9B
Upload scripts/compile_qwen3_5_9b.py with huggingface_hub
Browse files
scripts/compile_qwen3_5_9b.py
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@@ -108,10 +108,16 @@ import qai_hub as hub
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from huggingface_hub import login, HfApi
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login(token=HF_TOKEN)
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if hasattr(hub, "configure"):
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hub.configure(api_token=QAI_TOKEN)
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else:
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os.environ.setdefault("QAI_HUB_API_TOKEN", QAI_TOKEN)
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# ── helpers ────────────────────────────────────────────────────────────────────────
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from huggingface_hub import login, HfApi
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login(token=HF_TOKEN)
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os.environ["QAI_HUB_API_TOKEN"] = QAI_TOKEN
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cfg_dir = os.path.expanduser("~/.qai_hub")
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cfg_file = os.path.join(cfg_dir, "client.ini")
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if not os.path.exists(cfg_file):
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os.makedirs(cfg_dir, exist_ok=True)
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with open(cfg_file, "w") as f:
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f.write(f"[api]\napi_token = {QAI_TOKEN}\n")
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print(f" wrote {cfg_file}")
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if hasattr(hub, "configure"):
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hub.configure(api_token=QAI_TOKEN)
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# ── helpers ────────────────────────────────────────────────────────────────────────
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