Instructions to use Qwen/Qwen3.5-9B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Qwen/Qwen3.5-9B-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Qwen/Qwen3.5-9B-Base") 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, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Qwen/Qwen3.5-9B-Base") model = AutoModelForMultimodalLM.from_pretrained("Qwen/Qwen3.5-9B-Base", 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=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Qwen/Qwen3.5-9B-Base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Qwen/Qwen3.5-9B-Base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Qwen/Qwen3.5-9B-Base", "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/Qwen/Qwen3.5-9B-Base
- SGLang
How to use Qwen/Qwen3.5-9B-Base 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 "Qwen/Qwen3.5-9B-Base" \ --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": "Qwen/Qwen3.5-9B-Base", "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 "Qwen/Qwen3.5-9B-Base" \ --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": "Qwen/Qwen3.5-9B-Base", "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 Qwen/Qwen3.5-9B-Base with Docker Model Runner:
docker model run hf.co/Qwen/Qwen3.5-9B-Base
Clarification on whether Qwen3.5-9B-Base received any post-training or assistant-aligned mid-training
Hello Qwen team,
Thank you for releasing Qwen3.5-9B-Base. I'm evaluating it for foundation-model R&D where the starting checkpoint needs to be as close as possible to a clean base model. I hope to implement my own continued pretraining and full post-training stack, so I want to correctly understand which training stages were applied before this checkpoint was released. The model card describes this repository as the “pre-trained only model,” but the overview also lists “Training Stage: Pre-training & Post-training.” Could you clarify what this means for Qwen3.5-9B-Base specifically?
In particular, did the released 'Base' checkpoint receive any assistant-aligned or post-training stage before release, such as:
- supervised fine-tuning / instruction tuning
- assistant-response or chat tuning
- RLHF, RLAIF, DPO, GRPO, PPO, or other preference/RL optimization
- safety alignment
- reasoning or chain-of-thought tuning
- tool-use or agentic-behavior training
- chat-template behavioral tuning beyond tokenizer/control-token support
- continued pretraining or midtraining on instruction, chat, assistant-response, tool-use, or reasoning-solution data
I understand that continued pretraining or midtraining on raw/non-interactive corpora such as code, papers, math, documents, web text, or multilingual text may still be considered part of pretraining. My concern is specifically whether any deliberate assistant-aligned, instruction-style, preference-based, tool-use, or reasoning-solution training was applied to the 'Base' checkpoint, since that would confound independent post-training stack R&D.
I do not need proprietary details about the dataset or training recipe. A high-level clarification would be enough: Is Qwen3.5-9B-Base suitable as a starting checkpoint for independent continued pretraining and a fully custom post-training pipeline, assuming I need no prior deliberate assistant alignment, preference optimization, or instruction tuning to have been applied?
Thank you very much for clarifying!