Instructions to use palmfuture/Qwen3.6-35B-A3B-GPTQ-Int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use palmfuture/Qwen3.6-35B-A3B-GPTQ-Int4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="palmfuture/Qwen3.6-35B-A3B-GPTQ-Int4") 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("palmfuture/Qwen3.6-35B-A3B-GPTQ-Int4") model = AutoModelForMultimodalLM.from_pretrained("palmfuture/Qwen3.6-35B-A3B-GPTQ-Int4", 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 palmfuture/Qwen3.6-35B-A3B-GPTQ-Int4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "palmfuture/Qwen3.6-35B-A3B-GPTQ-Int4" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "palmfuture/Qwen3.6-35B-A3B-GPTQ-Int4", "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/palmfuture/Qwen3.6-35B-A3B-GPTQ-Int4
- SGLang
How to use palmfuture/Qwen3.6-35B-A3B-GPTQ-Int4 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 "palmfuture/Qwen3.6-35B-A3B-GPTQ-Int4" \ --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": "palmfuture/Qwen3.6-35B-A3B-GPTQ-Int4", "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 "palmfuture/Qwen3.6-35B-A3B-GPTQ-Int4" \ --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": "palmfuture/Qwen3.6-35B-A3B-GPTQ-Int4", "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 palmfuture/Qwen3.6-35B-A3B-GPTQ-Int4 with Docker Model Runner:
docker model run hf.co/palmfuture/Qwen3.6-35B-A3B-GPTQ-Int4
chat_template: replace no-user-query raise_exception with tolerant fallback
Browse filesThe multi_step_tool guard hard-raises when a conversation contains no plain user message (shape: [system, assistant, tool, ...]). Agent frameworks produce exactly this shape after history compression, and runtimes that render the template during automatic tool-parser generation (vLLM >= 0.24, llama.cpp, LM Studio, Ollama) fail the entire request with a 400 before generating a token — see vllm-project/vllm#36432 and Qwen/Qwen3.6-27B discussion #16 for the same failure on sibling models.
ns.last_query_index only gates reasoning-content preservation, so the safe fallback is -1 (treat every assistant turn as after the last query, i.e. preserve reasoning). Verified locally on vLLM 0.24: the failing shape renders and generates correctly with this template, and normal chat / multi-step tool conversations are byte-identical.
- chat_template.jinja +6 -1
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{%- endif %}
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{%- endfor %}
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{%- if ns.multi_step_tool %}
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{
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{%- endif %}
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{%- for message in messages %}
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{%- set content = render_content(message.content, true)|trim %}
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{%- endif %}
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{%- endfor %}
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{%- if ns.multi_step_tool %}
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{#- Tolerate conversations with no plain user turn (e.g. [system, assistant, tool, ...],
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common in agent frameworks after history compression). The hard raise breaks every
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runtime that renders the template at tool-parser-generation time (vLLM >=0.24,
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llama.cpp, LM Studio, Ollama): the request 400s before a single token is generated.
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-1 == "no user query" == preserve reasoning content on all assistant turns. #}
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{%- set ns.last_query_index = -1 %}
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{%- endif %}
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{%- for message in messages %}
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{%- set content = render_content(message.content, true)|trim %}
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